Title: | Sampling Variance Estimation |
---|---|
Description: | Functions to calculate some point estimators and estimate their variance under unequal probability sampling without replacement. Single and two-stage sampling designs are considered. Some approximations for the second-order inclusion probabilities (joint inclusion probabilities) are available (sample and population based). A variety of Jackknife variance estimators are implemented. Almost every function is written in C (compiled) code for faster results. The functions incorporate some performance improvements for faster results with large datasets. |
Authors: | Emilio Lopez Escobar [aut, cre, cph] <[email protected]>, Ernesto Barrios Zamudio [ctb] <[email protected]>, Juan Francisco Munoz Rosas [ctb] <[email protected]> |
Maintainer: | Emilio Lopez Escobar <[email protected]> |
License: | GPL (>= 2) |
Version: | 1.5 |
Built: | 2024-12-24 06:50:45 UTC |
Source: | CRAN |
The package contains functions to calculate some point estimators and estimate their variance under unequal probability sampling without replacement. Uni-stage and two-stage sampling designs are considered. The package further contains some approximations for the joint-inclusion probabilities (population and sample based formulae).
Emphasis has been put on the speed of routines as the package mostly uses C compiled code. Below there is a list of available functions. These are grouped in purpose lists, aiming to clarify their usage.
The user should pick a suitable combination of a population parameter of interest, a choice of point estimator, and a choice of variance estimator.
For these population parameters: | The available point estimators are: |
total: |
Est.Total.NHT |
Est.Total.Hajek |
|
mean: |
Est.Mean.NHT |
Est.Mean.Hajek |
|
empirical cumulative distribution function: |
Est.EmpDistFunc.NHT |
Est.EmpDistFunc.Hajek |
|
ratio: |
Est.Ratio |
correlation coefficient: |
Est.Corr.NHT |
Est.Corr.Hajek |
|
regression coefficients: |
Est.RegCoI.Hajek |
Est.RegCo.Hajek
|
For these point estimators: | The available variance estimators for self-weighted two-stage samples are: |
Est.Total.Hajek : |
VE.Jk.EB.SW2.Total.Hajek |
Est.Mean.Hajek : |
VE.Jk.EB.SW2.Mean.Hajek |
Est.Ratio : |
VE.Jk.EB.SW2.Ratio |
Est.Corr.Hajek : |
VE.Jk.EB.SW2.Corr.Hajek |
Est.RegCoI.Hajek : |
VE.Jk.EB.SW2.RegCoI.Hajek |
Est.RegCo.Hajek : |
VE.Jk.EB.SW2.RegCo.Hajek
|
For the inclusion probabilities: | The available functions are: |
1st order inclusion probabilities: |
Pk.PropNorm.U |
2nd order (joint) inclusion probabilities: |
Pkl.Hajek.s |
Pkl.Hajek.U
|
datasets |
oaxaca
|
To return to this description type:help(samplingVarEst)
or type:?samplingVarEst
To cite, use:citation("samplingVarEst")
Estimates a population correlation coefficient of two variables using the Hajek (1971) point estimator.
Est.Corr.Hajek(VecY.s, VecX.s, VecPk.s)
Est.Corr.Hajek(VecY.s, VecX.s, VecPk.s)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
For the population correlation coefficient of two variables and
:
the point estimator of , assuming that
is unknown (see Sarndal et al., 1992, Sec. 5.9) (implemented by the current function), is:
where is the Hajek (1971) point estimator of the population mean
,
and with
denoting the inclusion probability of the
-th element in the sample
.
The function returns a value for the correlation coefficient point estimator.
Emilio Lopez Escobar.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
Est.Corr.NHT
VE.Jk.Tukey.Corr.Hajek
VE.Jk.CBS.HT.Corr.Hajek
VE.Jk.CBS.SYG.Corr.Hajek
VE.Jk.B.Corr.Hajek
VE.Jk.EB.SW2.Corr.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the correlation coefficient estimator for y1 and x Est.Corr.Hajek(y1[s==1], x[s==1], pik.U[s==1]) #Computes the correlation coefficient estimator for y2 and x Est.Corr.Hajek(y2[s==1], x[s==1], pik.U[s==1])
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the correlation coefficient estimator for y1 and x Est.Corr.Hajek(y1[s==1], x[s==1], pik.U[s==1]) #Computes the correlation coefficient estimator for y2 and x Est.Corr.Hajek(y2[s==1], x[s==1], pik.U[s==1])
Estimates a population correlation coefficient of two variables using the Narain (1951); Horvitz-Thompson (1952) point estimator.
Est.Corr.NHT(VecY.s, VecX.s, VecPk.s, N)
Est.Corr.NHT(VecY.s, VecX.s, VecPk.s, N)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. |
For the population correlation coefficient of two variables and
:
the point estimator of (implemented by the current function) is given by:
where is the Narain (1951); Horvitz-Thompson (1952) estimator for the population mean
,
and with
denoting the inclusion probability of the
-th element in the sample
.
The function returns a value for the correlation coefficient point estimator.
Emilio Lopez Escobar.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Narain, R. D. (1951) On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–175.
Est.Corr.Hajek
VE.Jk.Tukey.Corr.NHT
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the correlation coefficient estimator for y1 and x Est.Corr.NHT(y1[s==1], x[s==1], pik.U[s==1], N) #Computes the correlation coefficient estimator for y2 and x Est.Corr.NHT(y2[s==1], x[s==1], pik.U[s==1], N)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the correlation coefficient estimator for y1 and x Est.Corr.NHT(y1[s==1], x[s==1], pik.U[s==1], N) #Computes the correlation coefficient estimator for y2 and x Est.Corr.NHT(y2[s==1], x[s==1], pik.U[s==1], N)
Computes the Hajek (1971) estimator for the empirical cumulative distribution function (ECDF).
Est.EmpDistFunc.Hajek(VecY.s, VecPk.s, t)
Est.EmpDistFunc.Hajek(VecY.s, VecPk.s, t)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
t |
value to be evaluated for the empirical cumulative distribution function. It must be an integer or a double-precision scalar. |
For the population empirical cumulative distribution function (ECDF) of the variable at the value
:
the approximately unbiased Hajek (1971) estimator of (implemented by the current function) is given by:
where denotes the indicator function that takes the value
if
and the value
otherwise, and where
and
denotes the inclusion probability of the
-th element in the sample
.
The function returns a value for the empirical cumulative distribution function evaluated at .
Emilio Lopez Escobar [aut, cre], Juan Francisco Munoz Rosas [ctb].
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
data(oaxaca) #Loads Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the inclusion probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 Est.EmpDistFunc.Hajek(y1[s==1], pik.U[s==1], 950) #Hajek est. of ECDF for y1 at t=950
data(oaxaca) #Loads Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the inclusion probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 Est.EmpDistFunc.Hajek(y1[s==1], pik.U[s==1], 950) #Hajek est. of ECDF for y1 at t=950
Computes the Narain (1951); Horvitz-Thompson (1952) estimator for the empirical cumulative distribution function (ECDF).
Est.EmpDistFunc.NHT(VecY.s, VecPk.s, N, t)
Est.EmpDistFunc.NHT(VecY.s, VecPk.s, N, t)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. |
t |
value to be evaluated for the empirical cumulative distribution function. It must be an integer or a double-precision scalar. |
For the population empirical cumulative distribution function (ECDF) of the variable at the value
:
the unbiased Narain (1951); Horvitz-Thompson (1952) estimator of (implemented by the current function) is given by:
where denotes the indicator function that takes the value
if
and the value
otherwise, and where
denotes the inclusion probability of the
-th element in the sample
.
The function returns a value for the empirical cumulative distribution function evaluated at .
Emilio Lopez Escobar [aut, cre], Juan Francisco Munoz Rosas [ctb].
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Narain, R. D. (1951) On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–175.
data(oaxaca) #Loads Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the inclusion probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 Est.EmpDistFunc.NHT(y1[s==1], pik.U[s==1], N, 950) #NHT est. of ECDF for y1 at t=950
data(oaxaca) #Loads Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the inclusion probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 Est.EmpDistFunc.NHT(y1[s==1], pik.U[s==1], N, 950) #NHT est. of ECDF for y1 at t=950
Computes the Hajek (1971) estimator for a population mean.
Est.Mean.Hajek(VecY.s, VecPk.s)
Est.Mean.Hajek(VecY.s, VecPk.s)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
For the population mean of the variable :
the approximately unbiased Hajek (1971) estimator of (implemented by the current function) is given by:
where and
denotes the inclusion probability of the
-th element in the sample
.
The function returns a value for the mean point estimator.
Emilio Lopez Escobar.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Est.Mean.NHT
VE.Jk.Tukey.Mean.Hajek
VE.Jk.CBS.HT.Mean.Hajek
VE.Jk.CBS.SYG.Mean.Hajek
VE.Jk.B.Mean.Hajek
VE.Jk.EB.SW2.Mean.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 Est.Mean.Hajek(y1[s==1], pik.U[s==1]) #Computes the Hajek est. for y1 Est.Mean.Hajek(y2[s==1], pik.U[s==1]) #Computes the Hajek est. for y2
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 Est.Mean.Hajek(y1[s==1], pik.U[s==1]) #Computes the Hajek est. for y1 Est.Mean.Hajek(y2[s==1], pik.U[s==1]) #Computes the Hajek est. for y2
Computes the Narain (1951); Horvitz-Thompson (1952) estimator for a population mean.
Est.Mean.NHT(VecY.s, VecPk.s, N)
Est.Mean.NHT(VecY.s, VecPk.s, N)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. |
For the population mean of the variable :
the unbiased Narain (1951); Horvitz-Thompson (1952) estimator of (implemented by the current function) is given by:
where denotes the inclusion probability of the
-th element in the sample
.
The function returns a value for the mean point estimator.
Emilio Lopez Escobar.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Narain, R. D. (1951) On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–175.
Est.Mean.Hajek
VE.HT.Mean.NHT
VE.SYG.Mean.NHT
VE.Hajek.Mean.NHT
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 Est.Mean.NHT(y1[s==1], pik.U[s==1], N) #The NHT estimator for y1 Est.Mean.NHT(y2[s==1], pik.U[s==1], N) #The NHT estimator for y2
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 Est.Mean.NHT(y1[s==1], pik.U[s==1], N) #The NHT estimator for y1 Est.Mean.NHT(y2[s==1], pik.U[s==1], N) #The NHT estimator for y2
Estimates a population ratio of two totals/means.
Est.Ratio(VecY.s, VecX.s, VecPk.s)
Est.Ratio(VecY.s, VecX.s, VecPk.s)
VecY.s |
vector of the numerator variable of interest; its length is equal to |
VecX.s |
vector of the denominator variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
For the population ratio of two totals/means of the variables and
:
the ratio estimator of (implemented by the current function) is given by:
where and
denotes the inclusion probability of the
-th element in the sample
.
The function returns a value for the ratio point estimator.
Emilio Lopez Escobar.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Narain, R. D. (1951) On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–175.
VE.Jk.Tukey.Ratio
VE.Jk.CBS.HT.Ratio
VE.Jk.CBS.SYG.Ratio
VE.Jk.B.Ratio
VE.Jk.EB.SW2.Ratio
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x Est.Ratio(y1[s==1], x[s==1], pik.U[s==1]) #Ratio estimator for y1 and x Est.Ratio(y2[s==1], x[s==1], pik.U[s==1]) #Ratio estimator for y2 and x
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x Est.Ratio(y1[s==1], x[s==1], pik.U[s==1]) #Ratio estimator for y1 and x Est.Ratio(y2[s==1], x[s==1], pik.U[s==1]) #Ratio estimator for y2 and x
Estimates the population regression coefficient using the Hajek (1971) point estimator.
Est.RegCo.Hajek(VecY.s, VecX.s, VecPk.s)
Est.RegCo.Hajek(VecY.s, VecX.s, VecPk.s)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
From Linear Regression Analysis, for an imposed population model
the population regression coefficient , assuming that the population size
is unknown (see Sarndal et al., 1992, Sec. 5.10), can be estimated by:
where and
are the Hajek (1971) point estimators of the population means
and
, respectively,
and with
denoting the inclusion probability of the
-th element in the sample
.
The function returns a value for the regression coefficient point estimator.
Emilio Lopez Escobar.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
Est.RegCoI.Hajek
VE.Jk.Tukey.RegCo.Hajek
VE.Jk.CBS.HT.RegCo.Hajek
VE.Jk.CBS.SYG.RegCo.Hajek
VE.Jk.B.RegCo.Hajek
VE.Jk.EB.SW2.RegCo.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the regression coefficient estimator for y1 and x Est.RegCo.Hajek(y1[s==1], x[s==1], pik.U[s==1]) #Computes the regression coefficient estimator for y2 and x Est.RegCo.Hajek(y2[s==1], x[s==1], pik.U[s==1])
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the regression coefficient estimator for y1 and x Est.RegCo.Hajek(y1[s==1], x[s==1], pik.U[s==1]) #Computes the regression coefficient estimator for y2 and x Est.RegCo.Hajek(y2[s==1], x[s==1], pik.U[s==1])
Estimates the population intercept regression coefficient using the Hajek (1971) point estimator.
Est.RegCoI.Hajek(VecY.s, VecX.s, VecPk.s)
Est.RegCoI.Hajek(VecY.s, VecX.s, VecPk.s)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
From Linear Regression Analysis, for an imposed population model
the population intercept regression coefficient , assuming that the population size
is unknown (see Sarndal et al., 1992, Sec. 5.10), can be estimated by:
where and
are the Hajek (1971) point estimators of the population means
and
, respectively,
and with
denoting the inclusion probability of the
-th element in the sample
.
The function returns a value for the intercept regression coefficient point estimator.
Emilio Lopez Escobar.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
Est.RegCo.Hajek
VE.Jk.Tukey.RegCoI.Hajek
VE.Jk.CBS.HT.RegCoI.Hajek
VE.Jk.CBS.SYG.RegCoI.Hajek
VE.Jk.B.RegCoI.Hajek
VE.Jk.EB.SW2.RegCoI.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the intercept regression coefficient estimator for y1 and x Est.RegCoI.Hajek(y1[s==1], x[s==1], pik.U[s==1]) #Computes the intercept regression coefficient estimator for y2 and x Est.RegCoI.Hajek(y2[s==1], x[s==1], pik.U[s==1])
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the intercept regression coefficient estimator for y1 and x Est.RegCoI.Hajek(y1[s==1], x[s==1], pik.U[s==1]) #Computes the intercept regression coefficient estimator for y2 and x Est.RegCoI.Hajek(y2[s==1], x[s==1], pik.U[s==1])
Computes the Hajek (1971) estimator for a population total.
Est.Total.Hajek(VecY.s, VecPk.s, N)
Est.Total.Hajek(VecY.s, VecPk.s, N)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. |
For the population total of the variable :
the approximately unbiased Hajek (1971) estimator of (implemented by the current function) is given by:
where and
denotes the inclusion probability of the
-th element in the sample
.
The function returns a value for the total point estimator.
Emilio Lopez Escobar.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Est.Total.NHT
VE.Jk.Tukey.Total.Hajek
VE.Jk.CBS.HT.Total.Hajek
VE.Jk.CBS.SYG.Total.Hajek
VE.Jk.B.Total.Hajek
VE.Jk.EB.SW2.Total.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable y1 y2 <- oaxaca$HOMES10 #Defines the variable y2 Est.Total.Hajek(y1[s==1], pik.U[s==1], N) #The Hajek estimator for y1 Est.Total.Hajek(y2[s==1], pik.U[s==1], N) #The Hajek estimator for y2
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable y1 y2 <- oaxaca$HOMES10 #Defines the variable y2 Est.Total.Hajek(y1[s==1], pik.U[s==1], N) #The Hajek estimator for y1 Est.Total.Hajek(y2[s==1], pik.U[s==1], N) #The Hajek estimator for y2
Computes the Narain (1951); Horvitz-Thompson (1952) estimator for a population total.
Est.Total.NHT(VecY.s, VecPk.s)
Est.Total.NHT(VecY.s, VecPk.s)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
For the population total of the variable :
the unbiased Narain (1951); Horvitz-Thompson (1952) estimator of (implemented by the current function) is given by:
where denotes the inclusion probability of the
-th element in the sample
.
The function returns a value for the total point estimator.
Emilio Lopez Escobar.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Narain, R. D. (1951) On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–175.
Est.Total.Hajek
VE.HT.Total.NHT
VE.SYG.Total.NHT
VE.Hajek.Total.NHT
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 Est.Total.NHT(y1[s==1], pik.U[s==1]) #Computes the NHT estimator for y1 Est.Total.NHT(y2[s==1], pik.U[s==1]) #Computes the NHT estimator for y2
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 Est.Total.NHT(y1[s==1], pik.U[s==1]) #Computes the NHT estimator for y1 Est.Total.NHT(y2[s==1], pik.U[s==1]) #Computes the NHT estimator for y2
Dataset with information about the free and sovereign state of Oaxaca, which is located in the southern part of Mexico. The dataset contains information on population, surface, indigenous language, agriculture, and income from years ranging from 2000 to 2010. The information was originally collected and processed by Mexico's National Institute of Statistics and Geography (INEGI by its name in Spanish, ‘Instituto Nacional de Estadistica y Geografia’, http://www.inegi.org.mx/).
data(oaxaca)
data(oaxaca)
A data frame with 570 observations on the following 41 variables:
region INEGI code.
region name (without accents and Spanish language characters).
district INEGI code.
district name (without accents and Spanish language characters).
municipality INEGI code.
municipality name (without accents and Spanish language characters).
surface in squared kilometres 2005.
population 2000.
population 2010.
number of homes 2000.
number of homes 2010.
male population 2000.
male population 2010.
female population 2000.
female population 2010.
5 or more years old population which speaks indigenous language 2000.
5 or more years old population which speaks indigenous language 2010.
gross income in thousands of Mexican pesos 2000.
gross income in thousands of Mexican pesos 2001.
gross income in thousands of Mexican pesos 2002.
gross income in thousands of Mexican pesos 2003.
planted trees 2000.
planted trees 2001.
planted trees 2002.
planted trees 2003.
marriages 2007.
marriages 2008.
marriages 2009.
harvested bean surface in hectares 2007.
harvested bean surface in hectares 2008.
harvested bean surface in hectares 2009.
value of bean production in thousands of Mexican pesos 2007.
value of bean production in thousands of Mexican pesos 2008.
value of bean production in thousands of Mexican pesos 2009.
volume of bean production in tons 2007.
volume of bean production in tons 2008.
volume of bean production in tons 2009.
a sample (column vector of ones and zeros; 1 = selected, 0 = otherwise) of 373 municipalities drawn using the Hajek (1964) maximum-entropy sampling design with inclusion probabilities proportional to the variable HOMES00.
a sample (column vector of ones and zeros; 1 = selected, 0 = otherwise) of 373 municipalities drawn using the Hajek (1964) maximum-entropy sampling design with inclusion probabilities proportional to the variable SURFAC05.
the size of the district, i.e., the number of municipalities in each district.
a sample (column vector of ones and zeros; 1 = selected, 0 = otherwise) of 30 municipalities drawn using a self-weighted two-stage sampling design. The first stage draws 10 districts using the Hajek (1964) maximum-entropy sampling design with clusters' inclusion probabilities proportional to the size of the clusters (variable SIZEDIST). The second stage draws 3 municipalities within the selected districts at the first stage, using equal-probability without-replacement sampling.
Mexico's National Institute of Statistics and Geography (INEGI), ‘Instituto Nacional de Estadistica y Geografia’ http://www.inegi.org.mx/
data(oaxaca) #Loads the Oaxaca municipalities dataset mean(oaxaca$INCOME00, na.rm= TRUE) #Computes INCOME00 mean (note it has NA's) median(oaxaca$INCOME00, na.rm= TRUE) #Computes INCOME00 median (note it has NA's)
data(oaxaca) #Loads the Oaxaca municipalities dataset mean(oaxaca$INCOME00, na.rm= TRUE) #Computes INCOME00 mean (note it has NA's) median(oaxaca$INCOME00, na.rm= TRUE) #Computes INCOME00 median (note it has NA's)
Creates and normalises the 1st order inclusion probabilities proportional to a specified variable. In the current context, normalisation means that the inclusion probabilities are less than or equal to 1. Ideally, they should sum up to , the sample size.
Pk.PropNorm.U(n, VecMOS.U)
Pk.PropNorm.U(n, VecMOS.U)
n |
the sample size. It must be an integer or a double-precision scalar with zero-valued fractional part. |
VecMOS.U |
vector of the variable called measure of size (MOS) to which the first-order inclusion probabilities are to be proportional; its length is equal to the population size. Values in VecMOS.U should be greater than zero (a warning message appears if this does not hold). There must not be missing values. |
Although the normalisation procedure is well-known in the survey sampling literature, we follow the procedure described in Chao (1982, p. 654). Hence, we obtain a unique set of inclusion probabilities that are proportional to the MOS variable.
The function returns a vector of length with the inclusion probabilities.
Emilio Lopez Escobar.
Chao, M. T. (1982) A general purpose unequal probability sampling plan. Biometrika 69, 653–656.
data(oaxaca) #Loads the Oaxaca municipalities dataset #Creates the normalised 1st order incl. probs. proportional #to the variable oaxaca$HOMES00 and with sample size 373 pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) sum(pik.U) #Shows the sum is equal to the sample size 373 any(pik.U>1) #Shows there isn't any probability greater than 1 any(pik.U<0) #Shows there isn't any probability less than 0
data(oaxaca) #Loads the Oaxaca municipalities dataset #Creates the normalised 1st order incl. probs. proportional #to the variable oaxaca$HOMES00 and with sample size 373 pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) sum(pik.U) #Shows the sum is equal to the sample size 373 any(pik.U>1) #Shows there isn't any probability greater than 1 any(pik.U<0) #Shows there isn't any probability less than 0
Computes the Hajek (1964) approximation for the 2nd order (joint) inclusion probabilities utilising only sample-based quantities.
Pkl.Hajek.s(VecPk.s)
Pkl.Hajek.s(VecPk.s)
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to the sample size. Values in |
Let denote the inclusion probability of the
-th element in the sample
, and let
denote the joint-inclusion probabilities of the
-th and
-th elements in the sample
. If the joint-inclusion probabilities
are not available, the Hajek (1964) approximation can be used. Note that this approximation is designed for large-entropy sampling designs, large samples, and large populations, i.e. care should be taken with highly-stratified samples, e.g. Berger (2005).
The sample-based version of the Hajek (1964) approximation for the joint-inclusion probabilities (implemented by the current function) is:
where .
The approximation was originally developed for , under the maximum-entropy sampling design (see Hajek 1981, Theorem 3.3, Ch. 3 and 6), the Rejective Sampling design. It requires that the utilised sampling design is of large entropy. An overview can be found in Berger and Tille (2009). An account of different sampling designs,
approximations, and approximate variances under large-entropy designs can be found in Tille (2006), Brewer and Donadio (2003), and Haziza, Mecatti, and Rao (2008). Recently, Berger (2011) gave sufficient conditions under which Hajek's results still hold for large-entropy sampling designs that are not the maximum-entropy one.
The function returns a ( by
) square matrix with the estimated joint inclusion probabilities, where
is the sample size.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Berger, Y. G. (2011) Asymptotic consistency under large entropy sampling designs with unequal probabilities. Pakistan Journal of Statististics, 27, 407–426.
Berger, Y. G. and Tille, Y. (2009) Sampling with unequal probabilities. In Sample Surveys: Design, Methods and Applications (eds. D. Pfeffermann and C. R. Rao), 39–54. Elsevier, Amsterdam.
Brewer, K. R. W. and Donadio, M. E. (2003) The large entropy variance of the Horvitz-Thompson estimator. Survey Methodology 29, 189–196.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
Hajek, J. (1981) Sampling From a Finite Population. Dekker, New York.
Haziza, D., Mecatti, F. and Rao, J. N. K. (2008) Evaluation of some approximate variance estimators under the Rao-Sampford unequal probability sampling design. Metron, LXVI, 91–108.
Tille, Y. (2006) Sampling Algorithms. Springer, New York.
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #First 5 rows/cols of (sample-based) 2nd order incl. probs. matrix pikl.s[1:5,1:5]
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #First 5 rows/cols of (sample-based) 2nd order incl. probs. matrix pikl.s[1:5,1:5]
Computes the Hajek (1964) approximation for the 2nd order (joint) inclusion probabilities utilising population-based quantities.
Pkl.Hajek.U(VecPk.U)
Pkl.Hajek.U(VecPk.U)
VecPk.U |
vector of the first-order inclusion probabilities; its length is equal to the population size. Values in |
Let denote the inclusion probability of the
-th element in the sample
, and let
denote the joint-inclusion probabilities of the
-th and
-th elements in the sample
. If the joint-inclusion probabilities
are not available, the Hajek (1964) approximation can be used. Note that this approximation is designed for large-entropy sampling designs, large samples, and large populations, i.e., care should be taken with highly-stratified samples, e.g. Berger (2005).
The population-based version of the Hajek (1964) approximation for the joint-inclusion probabilities (implemented by the current function) is:
where .
The approximation was originally developed for , under the maximum-entropy sampling design (see Hajek 1981, Theorem 3.3, Ch. 3 and 6), the Rejective Sampling design. It requires that the utilised sampling design is of large entropy. An overview can be found in Berger and Tille (2009). An account of different sampling designs,
approximations, and approximate variances under large-entropy designs can be found in Tille (2006), Brewer and Donadio (2003), and Haziza, Mecatti, and Rao (2008). Recently, Berger (2011) gave sufficient conditions under which Hajek's results still hold for large-entropy sampling designs that are not the maximum-entropy one.
The function returns a ( by
) square matrix with the estimated joint inclusion probabilities, where
is the population size.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Berger, Y. G. (2011) Asymptotic consistency under large entropy sampling designs with unequal probabilities. Pakistan Journal of Statististics, 27, 407–426.
Berger, Y. G. and Tille, Y. (2009) Sampling with unequal probabilities. In Sample Surveys: Design, Methods and Applications (eds. D. Pfeffermann and C. R. Rao), 39–54. Elsevier, Amsterdam.
Brewer, K. R. W. and Donadio, M. E. (2003) The large entropy variance of the Horvitz-Thompson estimator. Survey Methodology 29, 189–196.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
Hajek, J. (1981) Sampling From a Finite Population. Dekker, New York.
Haziza, D., Mecatti, F. and Rao, J. N. K. (2008) Evaluation of some approximate variance estimators under the Rao-Sampford unequal probability sampling design. Metron, LXVI, 91–108.
Tille, Y. (2006) Sampling Algorithms. Springer, New York.
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. #(This approximation is only suitable for large-entropy sampling designs) pikl.U <- Pkl.Hajek.U(pik.U) #Approximates 2nd order incl. probs. from U #First 5 rows/cols of (population-based) 2nd order incl. probs. matrix pikl.U[1:5,1:5]
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. #(This approximation is only suitable for large-entropy sampling designs) pikl.U <- Pkl.Hajek.U(pik.U) #Approximates 2nd order incl. probs. from U #First 5 rows/cols of (population-based) 2nd order incl. probs. matrix pikl.U[1:5,1:5]
Computes the Escobar-Berger (2013) unequal probability replicate variance estimator for the Hajek estimator of a mean. It uses the Horvitz-Thompson (1952) variance form.
VE.EB.HT.Mean.Hajek(VecY.s, VecPk.s, MatPkl.s, VecAlpha.s = rep.int(1, length(VecPk.s)))
VE.EB.HT.Mean.Hajek(VecY.s, VecPk.s, MatPkl.s, VecAlpha.s = rep.int(1, length(VecPk.s)))
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
VecAlpha.s |
vector of the |
For the population mean of the variable :
the approximately unbiased Hajek (1971) estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Escobar-Berger (2013) unequal probability replicate variance estimator (implemented by the current function):
where
for some (suggested to be 1, see below comments) and with
Regarding the value of , Escobar-Berger (2013) show that
is valid for
but conclude that
should be used as
corresponds to a naive biased and unstable jackknife. They recommend
or
. If
,
reduces to the Escobar-Berger (2011) jackknife. Using
approximates the empirical influence function, i.e. the Gateaux (1919) derivative, or Demnati-Rao (2004) linearisation variance estimators. The larger the
, the closer the approximation. Further, Escobar-Berger (2013) give an intuitive explanation of the replication method from a jackknife and bootstrap perspective.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Demnati, A. and Rao, J. N. K. (2004) Linearization variance estimators for survey data. Survey Methodology, 30, 17–26.
Escobar, E. L. and Berger, Y. G. (2011) Jackknife variance estimation for functions of Horvitz-Thompson estimators under unequal probability sampling without replacement. In Proceeding of the 58th World Statistics Congress. Dublin, Ireland: International Statistical Institute.
Escobar, E. L. and Berger, Y. G. (2013) A new replicate variance estimator for unequal probability sampling without replacement. Canadian Journal of Statistics 41, 3, 508–524.
Gateaux, R. (1919) Fonctions d'une infinite de variables indeependantes. Bulletin de la Societe Mathematique de France, 47, 70–96.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
VE.Jk.Tukey.Mean.Hajek
VE.Jk.CBS.SYG.Mean.Hajek
VE.Jk.B.Mean.Hajek
VE.Jk.EB.SW2.Mean.Hajek
VE.EB.SYG.Mean.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable y1 y2 <- oaxaca$POPMAL10 #Defines the variable y2 Alpha.s <- rep(2, times=373) #Defines the vector with Alpha values #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek mean point estimator using y1 VE.EB.HT.Mean.Hajek(y1[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the Hajek mean point estimator using y2 VE.EB.HT.Mean.Hajek(y2[s==1], pik.U[s==1], pikl.s, Alpha.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable y1 y2 <- oaxaca$POPMAL10 #Defines the variable y2 Alpha.s <- rep(2, times=373) #Defines the vector with Alpha values #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek mean point estimator using y1 VE.EB.HT.Mean.Hajek(y1[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the Hajek mean point estimator using y2 VE.EB.HT.Mean.Hajek(y2[s==1], pik.U[s==1], pikl.s, Alpha.s)
Computes the Escobar-Berger (2013) unequal probability replicate variance estimator for the estimator of a ratio of two totals/means. It uses the Horvitz-Thompson (1952) variance form.
VE.EB.HT.Ratio(VecY.s, VecX.s, VecPk.s, MatPkl.s, VecAlpha.s = rep.int(1, length(VecPk.s)))
VE.EB.HT.Ratio(VecY.s, VecX.s, VecPk.s, MatPkl.s, VecAlpha.s = rep.int(1, length(VecPk.s)))
VecY.s |
vector of the numerator variable of interest; its length is equal to |
VecX.s |
vector of the denominator variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
VecAlpha.s |
vector of the |
For the population ratio of two totals/means of the variables and
:
the ratio estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Escobar-Berger (2013) unequal probability replicate variance estimator (implemented by the current function):
where
for some (suggested to be 1, see below comments) and with
Regarding the value of , Escobar-Berger (2013) show that
is valid for
but conclude that
should be used as
corresponds to a naive biased and unstable jackknife. They recommend
or
. If
,
reduces to the Escobar-Berger (2011) jackknife. Using
approximates the empirical influence function, i.e. the Gateaux (1919) derivative, or Demnati-Rao (2004) linearisation variance estimators. The larger the
, the closer the approximation. Further, Escobar-Berger (2013) give an intuitive explanation of the replication method from a jackknife and bootstrap perspective.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Demnati, A. and Rao, J. N. K. (2004) Linearization variance estimators for survey data. Survey Methodology, 30, 17–26.
Escobar, E. L. and Berger, Y. G. (2011) Jackknife variance estimation for functions of Horvitz-Thompson estimators under unequal probability sampling without replacement. In Proceeding of the 58th World Statistics Congress. Dublin, Ireland: International Statistical Institute.
Escobar, E. L. and Berger, Y. G. (2013) A new replicate variance estimator for unequal probability sampling without replacement. Canadian Journal of Statistics 41, 3, 508–524.
Gateaux, R. (1919) Fonctions d'une infinite de variables indeependantes. Bulletin de la Societe Mathematique de France, 47, 70–96.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
VE.Lin.HT.Ratio
VE.Lin.SYG.Ratio
VE.Jk.Tukey.Ratio
VE.Jk.CBS.HT.Ratio
VE.Jk.CBS.SYG.Ratio
VE.Jk.B.Ratio
VE.Jk.EB.SW2.Ratio
VE.EB.SYG.Ratio
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x Alpha.s <- rep(2, times=373) #Defines the vector with Alpha values #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the ratio point estimator using y1 VE.EB.HT.Ratio(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Using default VecAlpha.s #Computes the var. est. of the ratio point estimator using y2 VE.EB.HT.Ratio(y2[s==1], x[s==1], pik.U[s==1], pikl.s, Alpha.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x Alpha.s <- rep(2, times=373) #Defines the vector with Alpha values #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the ratio point estimator using y1 VE.EB.HT.Ratio(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Using default VecAlpha.s #Computes the var. est. of the ratio point estimator using y2 VE.EB.HT.Ratio(y2[s==1], x[s==1], pik.U[s==1], pikl.s, Alpha.s)
Computes the Escobar-Berger (2013) unequal probability replicate variance estimator for the Hajek estimator of a total. It uses the Horvitz-Thompson (1952) variance form.
VE.EB.HT.Total.Hajek(VecY.s, VecPk.s, MatPkl.s, N, VecAlpha.s = rep.int(1, length(VecPk.s)))
VE.EB.HT.Total.Hajek(VecY.s, VecPk.s, MatPkl.s, N, VecAlpha.s = rep.int(1, length(VecPk.s)))
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. |
VecAlpha.s |
vector of the |
For the population total of the variable :
the approximately unbiased Hajek (1971) estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Escobar-Berger (2013) unequal probability replicate variance estimator (implemented by the current function):
where
for some (suggested to be 1, see below comments) and with
Regarding the value of , Escobar-Berger (2013) show that
is valid for
but conclude that
should be used as
corresponds to a naive biased and unstable jackknife. They recommend
or
. If
,
reduces to the Escobar-Berger (2011) jackknife. Using
approximates the empirical influence function, i.e. the Gateaux (1919) derivative, or Demnati-Rao (2004) linearisation variance estimators. The larger the
, the closer the approximation. Further, Escobar-Berger (2013) give an intuitive explanation of the replication method from a jackknife and bootstrap perspective.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Demnati, A. and Rao, J. N. K. (2004) Linearization variance estimators for survey data. Survey Methodology, 30, 17–26.
Escobar, E. L. and Berger, Y. G. (2011) Jackknife variance estimation for functions of Horvitz-Thompson estimators under unequal probability sampling without replacement. In Proceeding of the 58th World Statistics Congress. Dublin, Ireland: International Statistical Institute.
Escobar, E. L. and Berger, Y. G. (2013) A new replicate variance estimator for unequal probability sampling without replacement. Canadian Journal of Statistics 41, 3, 508–524.
Gateaux, R. (1919) Fonctions d'une infinite de variables indeependantes. Bulletin de la Societe Mathematique de France, 47, 70–96.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
VE.Jk.Tukey.Total.Hajek
VE.Jk.CBS.SYG.Total.Hajek
VE.Jk.B.Total.Hajek
VE.Jk.EB.SW2.Total.Hajek
VE.EB.SYG.Total.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 Alpha.s <- rep(2, times=373) #Defines the vector with Alpha values #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek total point estimator using y1 VE.EB.HT.Total.Hajek(y1[s==1], pik.U[s==1], pikl.s, N) #Computes the var. est. of the Hajek total point estimator using y2 VE.EB.HT.Total.Hajek(y2[s==1], pik.U[s==1], pikl.s, N, Alpha.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 Alpha.s <- rep(2, times=373) #Defines the vector with Alpha values #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek total point estimator using y1 VE.EB.HT.Total.Hajek(y1[s==1], pik.U[s==1], pikl.s, N) #Computes the var. est. of the Hajek total point estimator using y2 VE.EB.HT.Total.Hajek(y2[s==1], pik.U[s==1], pikl.s, N, Alpha.s)
Computes the Escobar-Berger (2013) unequal probability replicate variance estimator for the Hajek estimator of a mean. It uses the Sen (1953); Yates-Grundy(1953) variance form.
VE.EB.SYG.Mean.Hajek(VecY.s, VecPk.s, MatPkl.s, VecAlpha.s = rep.int(1, length(VecPk.s)))
VE.EB.SYG.Mean.Hajek(VecY.s, VecPk.s, MatPkl.s, VecAlpha.s = rep.int(1, length(VecPk.s)))
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
VecAlpha.s |
vector of the |
For the population mean of the variable :
the approximately unbiased Hajek (1971) estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Escobar-Berger (2013) unequal probability replicate variance estimator (implemented by the current function):
where
for some (suggested to be 1, see below comments) and with
Regarding the value of , Escobar-Berger (2013) show that
is valid for
but conclude that
should be used as
corresponds to a naive biased and unstable jackknife. They recommend
or
. If
,
reduces to the Escobar-Berger (2011) jackknife. Using
approximates the empirical influence function, i.e. the Gateaux (1919) derivative, or Demnati-Rao (2004) linearisation variance estimators. The larger the
, the closer the approximation. Further, Escobar-Berger (2013) give an intuitive explanation of the replication method from a jackknife and bootstrap perspective.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Demnati, A. and Rao, J. N. K. (2004) Linearization variance estimators for survey data. Survey Methodology, 30, 17–26.
Escobar, E. L. and Berger, Y. G. (2011) Jackknife variance estimation for functions of Horvitz-Thompson estimators under unequal probability sampling without replacement. In Proceeding of the 58th World Statistics Congress. Dublin, Ireland: International Statistical Institute.
Escobar, E. L. and Berger, Y. G. (2013) A new replicate variance estimator for unequal probability sampling without replacement. Canadian Journal of Statistics 41, 3, 508–524.
Gateaux, R. (1919) Fonctions d'une infinite de variables indeependantes. Bulletin de la Societe Mathematique de France, 47, 70–96.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sen, A. R. (1953) On the estimate of the variance in sampling with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 5, 119–127.
Yates, F. and Grundy, P. M. (1953) Selection without replacement from within strata with probability proportional to size. Journal of the Royal Statistical Society B, 15, 253–261.
VE.Jk.Tukey.Mean.Hajek
VE.Jk.CBS.HT.Mean.Hajek
VE.Jk.B.Mean.Hajek
VE.Jk.EB.SW2.Mean.Hajek
VE.EB.HT.Mean.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 Alpha.s <- rep(2, times=373) #Defines the vector with Alpha values #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek mean point estimator using y1 VE.EB.SYG.Mean.Hajek(y1[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the Hajek mean point estimator using y2 VE.EB.SYG.Mean.Hajek(y2[s==1], pik.U[s==1], pikl.s, Alpha.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 Alpha.s <- rep(2, times=373) #Defines the vector with Alpha values #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek mean point estimator using y1 VE.EB.SYG.Mean.Hajek(y1[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the Hajek mean point estimator using y2 VE.EB.SYG.Mean.Hajek(y2[s==1], pik.U[s==1], pikl.s, Alpha.s)
Computes the Escobar-Berger (2013) unequal probability replicate variance estimator for the estimator of a ratio of two totals/means. It uses the Sen (1953); Yates-Grundy(1953) variance form.
VE.EB.SYG.Ratio(VecY.s, VecX.s, VecPk.s, MatPkl.s, VecAlpha.s = rep.int(1, length(VecPk.s)))
VE.EB.SYG.Ratio(VecY.s, VecX.s, VecPk.s, MatPkl.s, VecAlpha.s = rep.int(1, length(VecPk.s)))
VecY.s |
vector of the numerator variable of interest; its length is equal to |
VecX.s |
vector of the denominator variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
VecAlpha.s |
vector of the |
For the population ratio of two totals/means of the variables and
:
the ratio estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Escobar-Berger (2013) unequal probability replicate variance estimator (implemented by the current function):
where
for some (suggested to be 1, see below comments) and with
Regarding the value of , Escobar-Berger (2013) show that
is valid for
but conclude that
should be used as
corresponds to a naive biased and unstable jackknife. They recommend
or
. If
,
reduces to the Escobar-Berger (2011) jackknife. Using
approximates the empirical influence function, i.e. the Gateaux (1919) derivative, or Demnati-Rao (2004) linearisation variance estimators. The larger the
, the closer the approximation. Further, Escobar-Berger (2013) give an intuitive explanation of the replication method from a jackknife and bootstrap perspective.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Demnati, A. and Rao, J. N. K. (2004) Linearization variance estimators for survey data. Survey Methodology, 30, 17–26.
Escobar, E. L. and Berger, Y. G. (2011) Jackknife variance estimation for functions of Horvitz-Thompson estimators under unequal probability sampling without replacement. In Proceeding of the 58th World Statistics Congress. Dublin, Ireland: International Statistical Institute.
Escobar, E. L. and Berger, Y. G. (2013) A new replicate variance estimator for unequal probability sampling without replacement. Canadian Journal of Statistics 41, 3, 508–524.
Gateaux, R. (1919) Fonctions d'une infinite de variables indeependantes. Bulletin de la Societe Mathematique de France, 47, 70–96.
Sen, A. R. (1953) On the estimate of the variance in sampling with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 5, 119–127.
Yates, F. and Grundy, P. M. (1953) Selection without replacement from within strata with probability proportional to size. Journal of the Royal Statistical Society B, 15, 253–261.
VE.Lin.HT.Ratio
VE.Lin.SYG.Ratio
VE.Jk.Tukey.Ratio
VE.Jk.CBS.HT.Ratio
VE.Jk.CBS.SYG.Ratio
VE.Jk.B.Ratio
VE.Jk.EB.SW2.Ratio
VE.EB.HT.Ratio
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x Alpha.s <- rep(2, times=373) #Defines the vector with Alpha values #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the ratio point estimator using y1 VE.EB.SYG.Ratio(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Using default VecAlpha.s #Computes the var. est. of the ratio point estimator using y2 VE.EB.SYG.Ratio(y2[s==1], x[s==1], pik.U[s==1], pikl.s, Alpha.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x Alpha.s <- rep(2, times=373) #Defines the vector with Alpha values #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the ratio point estimator using y1 VE.EB.SYG.Ratio(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Using default VecAlpha.s #Computes the var. est. of the ratio point estimator using y2 VE.EB.SYG.Ratio(y2[s==1], x[s==1], pik.U[s==1], pikl.s, Alpha.s)
Computes the Escobar-Berger (2013) unequal probability replicate variance estimator for the Hajek estimator of a total. It uses the Sen (1953); Yates-Grundy(1953) variance form.
VE.EB.SYG.Total.Hajek(VecY.s, VecPk.s, MatPkl.s, N, VecAlpha.s = rep.int(1, length(VecPk.s)))
VE.EB.SYG.Total.Hajek(VecY.s, VecPk.s, MatPkl.s, N, VecAlpha.s = rep.int(1, length(VecPk.s)))
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. |
VecAlpha.s |
vector of the |
For the population total of the variable :
the approximately unbiased Hajek (1971) estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Escobar-Berger (2013) unequal probability replicate variance estimator (implemented by the current function):
where
for some (suggested to be 1, see below comments) and with
Regarding the value of , Escobar-Berger (2013) show that
is valid for
but conclude that
should be used as
corresponds to a naive biased and unstable jackknife. They recommend
or
. If
,
reduces to the Escobar-Berger (2011) jackknife. Using
approximates the empirical influence function, i.e. the Gateaux (1919) derivative, or Demnati-Rao (2004) linearisation variance estimators. The larger the
, the closer the approximation. Further, Escobar-Berger (2013) give an intuitive explanation of the replication method from a jackknife and bootstrap perspective.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Demnati, A. and Rao, J. N. K. (2004) Linearization variance estimators for survey data. Survey Methodology, 30, 17–26.
Escobar, E. L. and Berger, Y. G. (2011) Jackknife variance estimation for functions of Horvitz-Thompson estimators under unequal probability sampling without replacement. In Proceeding of the 58th World Statistics Congress. Dublin, Ireland: International Statistical Institute.
Escobar, E. L. and Berger, Y. G. (2013) A new replicate variance estimator for unequal probability sampling without replacement. Canadian Journal of Statistics 41, 3, 508–524.
Gateaux, R. (1919) Fonctions d'une infinite de variables indeependantes. Bulletin de la Societe Mathematique de France, 47, 70–96.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sen, A. R. (1953) On the estimate of the variance in sampling with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 5, 119–127.
Yates, F. and Grundy, P. M. (1953) Selection without replacement from within strata with probability proportional to size. Journal of the Royal Statistical Society B, 15, 253–261.
VE.Jk.Tukey.Total.Hajek
VE.Jk.CBS.HT.Total.Hajek
VE.Jk.B.Total.Hajek
VE.Jk.EB.SW2.Total.Hajek
VE.EB.SYG.Total.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 Alpha.s <- rep(2, times=373) #Defines the vector with Alpha values #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek total point estimator using y1 VE.EB.SYG.Total.Hajek(y1[s==1], pik.U[s==1], pikl.s, N) #Computes the var. est. of the Hajek total point estimator using y2 VE.EB.SYG.Total.Hajek(y2[s==1], pik.U[s==1], pikl.s, N, Alpha.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 Alpha.s <- rep(2, times=373) #Defines the vector with Alpha values #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek total point estimator using y1 VE.EB.SYG.Total.Hajek(y1[s==1], pik.U[s==1], pikl.s, N) #Computes the var. est. of the Hajek total point estimator using y2 VE.EB.SYG.Total.Hajek(y2[s==1], pik.U[s==1], pikl.s, N, Alpha.s)
Computes the Hajek (1964) variance estimator for the Narain (1951); Horvitz-Thompson (1952) point estimator for a population mean.
VE.Hajek.Mean.NHT(VecY.s, VecPk.s, N)
VE.Hajek.Mean.NHT(VecY.s, VecPk.s, N)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. |
For the population mean of the variable :
the unbiased Narain (1951); Horvitz-Thompson (1952) estimator of is given by:
where denotes the inclusion probability of the
-th element in the sample
. For large-entropy sampling designs, the variance of
is approximated by the Hajek (1964) variance:
with and
.
The variance can be estimated by the variance estimator (implemented by the current function):
where and
.
Note that the Hajek (1964) variance approximation is designed for large-entropy sampling designs, large samples, and large populations, i.e., care should be taken with highly-stratified samples, e.g. Berger (2005).
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Narain, R. D. (1951) On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–175.
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 #Computes the (approximate) var. est. of the NHT point est. for y1 VE.Hajek.Mean.NHT(y1[s==1], pik.U[s==1], N) #Computes the (approximate) var. est. of the NHT point est. for y2 VE.Hajek.Mean.NHT(y2[s==1], pik.U[s==1], N)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 #Computes the (approximate) var. est. of the NHT point est. for y1 VE.Hajek.Mean.NHT(y1[s==1], pik.U[s==1], N) #Computes the (approximate) var. est. of the NHT point est. for y2 VE.Hajek.Mean.NHT(y2[s==1], pik.U[s==1], N)
Computes the Hajek (1964) variance estimator for the Narain (1951); Horvitz-Thompson (1952) point estimator for a population total.
VE.Hajek.Total.NHT(VecY.s, VecPk.s)
VE.Hajek.Total.NHT(VecY.s, VecPk.s)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
For the population total of the variable :
the unbiased Narain (1951); Horvitz-Thompson (1952) estimator of is given by:
where denotes the inclusion probability of the
-th element in the sample
. For large-entropy sampling designs, the variance of
is approximated by the Hajek (1964) variance:
with and
.
The variance can be estimated by the variance estimator (implemented by the current function):
where and
.
Note that the Hajek (1964) variance approximation is designed for large-entropy sampling designs, large samples, and large populations, i.e., care should be taken with highly-stratified samples, e.g. Berger (2005).
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Narain, R. D. (1951) On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–175.
VE.HT.Total.NHT
VE.SYG.Total.NHT
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$SURFAC05) #Reconstructs the 1st order incl. probs. s <- oaxaca$sSURFAC #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 #Computes the (approximate) var. est. of the NHT point est. from y1 VE.Hajek.Total.NHT(y1[s==1], pik.U[s==1]) #Computes the (approximate) var. est. of the NHT point est. from y2 VE.Hajek.Total.NHT(y2[s==1], pik.U[s==1])
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$SURFAC05) #Reconstructs the 1st order incl. probs. s <- oaxaca$sSURFAC #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 #Computes the (approximate) var. est. of the NHT point est. from y1 VE.Hajek.Total.NHT(y1[s==1], pik.U[s==1]) #Computes the (approximate) var. est. of the NHT point est. from y2 VE.Hajek.Total.NHT(y2[s==1], pik.U[s==1])
Computes the Horvitz-Thompson (1952) variance estimator for the Narain (1951); Horvitz-Thompson (1952) point estimator for a population mean.
VE.HT.Mean.NHT(VecY.s, VecPk.s, MatPkl.s, N)
VE.HT.Mean.NHT(VecY.s, VecPk.s, MatPkl.s, N)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. |
For the population mean of the variable :
the unbiased Narain (1951); Horvitz-Thompson (1952) estimator of is given by:
where denotes the inclusion probability of the
-th element in the sample
. Let
denotes the joint-inclusion probabilities of the
-th and
-th elements in the sample
. The variance of
is given by:
which can therefore be estimated by the Horvitz-Thompson variance estimator (implemented by the current function):
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Narain, R. D. (1951) On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–175.
VE.SYG.Mean.NHT
VE.Hajek.Mean.NHT
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$SURFAC05) #Reconstructs the 1st order incl. probs. s <- oaxaca$sSURFAC #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the variance estimation of the NHT point estimator for y1 VE.HT.Mean.NHT(y1[s==1], pik.U[s==1], pikl.s, N) #Computes the variance estimation of the NHT point estimator for y2 VE.HT.Mean.NHT(y2[s==1], pik.U[s==1], pikl.s, N)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$SURFAC05) #Reconstructs the 1st order incl. probs. s <- oaxaca$sSURFAC #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the variance estimation of the NHT point estimator for y1 VE.HT.Mean.NHT(y1[s==1], pik.U[s==1], pikl.s, N) #Computes the variance estimation of the NHT point estimator for y2 VE.HT.Mean.NHT(y2[s==1], pik.U[s==1], pikl.s, N)
Computes the Horvitz-Thompson (1952) variance estimator for the Narain (1951); Horvitz-Thompson (1952) point estimator for a population total.
VE.HT.Total.NHT(VecY.s, VecPk.s, MatPkl.s)
VE.HT.Total.NHT(VecY.s, VecPk.s, MatPkl.s)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
For the population total of the variable :
the unbiased Narain (1951); Horvitz-Thompson (1952) estimator of is given by:
where denotes the inclusion probability of the
-th element in the sample
. Let
denotes the joint-inclusion probabilities of the
-th and
-th elements in the sample
. The variance of
is given by:
which can therefore be estimated by the Horvitz-Thompson variance estimator (implemented by the current function):
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Narain, R. D. (1951) On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–175.
VE.SYG.Total.NHT
VE.Hajek.Total.NHT
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the NHT point estimator for y1 VE.HT.Total.NHT(y1[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the NHT point estimator for y2 VE.HT.Total.NHT(y2[s==1], pik.U[s==1], pikl.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the NHT point estimator for y1 VE.HT.Total.NHT(y1[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the NHT point estimator for y2 VE.HT.Total.NHT(y2[s==1], pik.U[s==1], pikl.s)
Computes the Berger (2007) unequal probability jackknife variance estimator for the estimator of a correlation coefficient of two variables using the Hajek (1971) point estimator.
VE.Jk.B.Corr.Hajek(VecY.s, VecX.s, VecPk.s)
VE.Jk.B.Corr.Hajek(VecY.s, VecX.s, VecPk.s)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
For the population correlation coefficient of two variables and
:
the point estimator of , assuming that
is unknown (see Sarndal et al., 1992, Sec. 5.9), is:
where is the Hajek (1971) point estimator of the population mean
,
and with
denoting the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Berger (2007) unequal probability jackknife variance estimator (implemented by the current function):
where
and
with
and where has the same functional form as
but omitting the
-th element from the sample
.
Note that this variance estimator implicitly utilises the Hajek (1964) approximations that are designed for large-entropy sampling designs, large samples, and large populations, i.e., care should be taken with highly-stratified samples, e.g. Berger (2005).
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Berger, Y. G. (2007) A jackknife variance estimator for unistage stratified samples with unequal probabilities. Biometrika 94, 953–964.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
VE.Jk.Tukey.Corr.Hajek
VE.Jk.CBS.HT.Corr.Hajek
VE.Jk.CBS.SYG.Corr.Hajek
VE.Jk.EB.SW2.Corr.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the var. est. of the corr. coeff. point estimator using y1 VE.Jk.B.Corr.Hajek(y1[s==1], x[s==1], pik.U[s==1]) #Computes the var. est. of the corr. coeff. point estimator using y2 VE.Jk.B.Corr.Hajek(y2[s==1], x[s==1], pik.U[s==1])
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the var. est. of the corr. coeff. point estimator using y1 VE.Jk.B.Corr.Hajek(y1[s==1], x[s==1], pik.U[s==1]) #Computes the var. est. of the corr. coeff. point estimator using y2 VE.Jk.B.Corr.Hajek(y2[s==1], x[s==1], pik.U[s==1])
Computes the Berger (2007) unequal probability jackknife variance estimator for the Hajek (1971) estimator of a mean.
VE.Jk.B.Mean.Hajek(VecY.s, VecPk.s)
VE.Jk.B.Mean.Hajek(VecY.s, VecPk.s)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
For the population mean of the variable :
the approximately unbiased Hajek (1971) estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Berger (2007) unequal probability jackknife variance estimator (implemented by the current function):
where
and
with
and
Note that this variance estimator implicitly utilises the Hajek (1964) approximations that are designed for large-entropy sampling designs, large samples, and large populations, i.e., care should be taken with highly-stratified samples, e.g. Berger (2005).
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Berger, Y. G. (2007) A jackknife variance estimator for unistage stratified samples with unequal probabilities. Biometrika 94, 953–964.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
VE.Jk.Tukey.Mean.Hajek
VE.Jk.CBS.HT.Mean.Hajek
VE.Jk.CBS.SYG.Mean.Hajek
VE.Jk.EB.SW2.Mean.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #Computes the var. est. of the Hajek mean point estimator using y1 VE.Jk.B.Mean.Hajek(y1[s==1], pik.U[s==1]) #Computes the var. est. of the Hajek mean point estimator using y2 VE.Jk.B.Mean.Hajek(y2[s==1], pik.U[s==1])
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #Computes the var. est. of the Hajek mean point estimator using y1 VE.Jk.B.Mean.Hajek(y1[s==1], pik.U[s==1]) #Computes the var. est. of the Hajek mean point estimator using y2 VE.Jk.B.Mean.Hajek(y2[s==1], pik.U[s==1])
Computes the Berger (2007) unequal probability jackknife variance estimator for the estimator of a ratio of two totals/means.
VE.Jk.B.Ratio(VecY.s, VecX.s, VecPk.s)
VE.Jk.B.Ratio(VecY.s, VecX.s, VecPk.s)
VecY.s |
vector of the numerator variable of interest; its length is equal to |
VecX.s |
vector of the denominator variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
For the population ratio of two totals/means of the variables and
:
the ratio estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Berger (2007) unequal probability jackknife variance estimator (implemented by the current function):
where
and
with
and
Note that this variance estimator implicitly utilises the Hajek (1964) approximations that are designed for large-entropy sampling designs, large samples, and large populations, i.e., care should be taken with highly-stratified samples, e.g. Berger (2005).
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Berger, Y. G. (2007) A jackknife variance estimator for unistage stratified samples with unequal probabilities. Biometrika 94, 953–964.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
VE.Lin.HT.Ratio
VE.Lin.SYG.Ratio
VE.Jk.Tukey.Ratio
VE.Jk.CBS.HT.Ratio
VE.Jk.CBS.SYG.Ratio
VE.Jk.EB.SW2.Ratio
VE.EB.HT.Ratio
VE.EB.SYG.Ratio
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x #Computes the var. est. of the ratio point estimator using y1 VE.Jk.B.Ratio(y1[s==1], x[s==1], pik.U[s==1]) #Computes the var. est. of the ratio point estimator using y2 VE.Jk.B.Ratio(y2[s==1], x[s==1], pik.U[s==1])
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x #Computes the var. est. of the ratio point estimator using y1 VE.Jk.B.Ratio(y1[s==1], x[s==1], pik.U[s==1]) #Computes the var. est. of the ratio point estimator using y2 VE.Jk.B.Ratio(y2[s==1], x[s==1], pik.U[s==1])
Computes the Berger (2007) unequal probability jackknife variance estimator for the estimator of the regression coefficient using the Hajek (1971) point estimator.
VE.Jk.B.RegCo.Hajek(VecY.s, VecX.s, VecPk.s)
VE.Jk.B.RegCo.Hajek(VecY.s, VecX.s, VecPk.s)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
From Linear Regression Analysis, for an imposed population model
the population regression coefficient , assuming that the population size
is unknown (see Sarndal et al., 1992, Sec. 5.10), can be estimated by:
where and
are the Hajek (1971) point estimators of the population means
and
, respectively,
and with
denoting the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Berger (2007) unequal probability jackknife variance estimator (implemented by the current function):
where
and
with
and where has the same functional form as
but omitting the
-th element from the sample
.
Note that this variance estimator implicitly utilises the Hajek (1964) approximations that are designed for large-entropy sampling designs, large samples, and large populations, i.e., care should be taken with highly-stratified samples, e.g. Berger (2005).
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Berger, Y. G. (2007) A jackknife variance estimator for unistage stratified samples with unequal probabilities. Biometrika 94, 953–964.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
VE.Jk.B.RegCoI.Hajek
VE.Jk.Tukey.RegCo.Hajek
VE.Jk.CBS.HT.RegCo.Hajek
VE.Jk.CBS.SYG.RegCo.Hajek
VE.Jk.EB.SW2.RegCo.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the var. est. of the regression coeff. point estimator using y1 VE.Jk.B.RegCo.Hajek(y1[s==1], x[s==1], pik.U[s==1]) #Computes the var. est. of the regression coeff. point estimator using y2 VE.Jk.B.RegCo.Hajek(y2[s==1], x[s==1], pik.U[s==1])
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the var. est. of the regression coeff. point estimator using y1 VE.Jk.B.RegCo.Hajek(y1[s==1], x[s==1], pik.U[s==1]) #Computes the var. est. of the regression coeff. point estimator using y2 VE.Jk.B.RegCo.Hajek(y2[s==1], x[s==1], pik.U[s==1])
Computes the Berger (2007) unequal probability jackknife variance estimator for the estimator of the intercept regression coefficient using the Hajek (1971) point estimator.
VE.Jk.B.RegCoI.Hajek(VecY.s, VecX.s, VecPk.s)
VE.Jk.B.RegCoI.Hajek(VecY.s, VecX.s, VecPk.s)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
From Linear Regression Analysis, for an imposed population model
the population intercept regression coefficient , assuming that the population size
is unknown (see Sarndal et al., 1992, Sec. 5.10), can be estimated by:
where and
are the Hajek (1971) point estimators of the population means
and
, respectively,
and with
denoting the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Berger (2007) unequal probability jackknife variance estimator (implemented by the current function):
where
and
with
and where has the same functional form as
but omitting the
-th element from the sample
.
Note that this variance estimator implicitly utilises the Hajek (1964) approximations that are designed for large-entropy sampling designs, large samples, and large populations, i.e., care should be taken with highly-stratified samples, e.g. Berger (2005).
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Berger, Y. G. (2007) A jackknife variance estimator for unistage stratified samples with unequal probabilities. Biometrika 94, 953–964.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
VE.Jk.B.RegCo.Hajek
VE.Jk.Tukey.RegCoI.Hajek
VE.Jk.CBS.HT.RegCoI.Hajek
VE.Jk.CBS.SYG.RegCoI.Hajek
VE.Jk.EB.SW2.RegCoI.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the var. est. of the intercept reg. coeff. point estimator using y1 VE.Jk.B.RegCoI.Hajek(y1[s==1], x[s==1], pik.U[s==1]) #Computes the var. est. of the intercept reg. coeff. point estimator using y2 VE.Jk.B.RegCoI.Hajek(y2[s==1], x[s==1], pik.U[s==1])
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the var. est. of the intercept reg. coeff. point estimator using y1 VE.Jk.B.RegCoI.Hajek(y1[s==1], x[s==1], pik.U[s==1]) #Computes the var. est. of the intercept reg. coeff. point estimator using y2 VE.Jk.B.RegCoI.Hajek(y2[s==1], x[s==1], pik.U[s==1])
Computes the Berger (2007) unequal probability jackknife variance estimator for the Hajek (1971) estimator of a total.
VE.Jk.B.Total.Hajek(VecY.s, VecPk.s, N)
VE.Jk.B.Total.Hajek(VecY.s, VecPk.s, N)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. |
For the population total of the variable :
the approximately unbiased Hajek (1971) estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Berger (2007) unequal probability jackknife variance estimator (implemented by the current function):
where
and
with
and
Note that this variance estimator implicitly utilises the Hajek (1964) approximations that are designed for large-entropy sampling designs, large samples, and large populations, i.e., care should be taken with highly-stratified samples, e.g. Berger (2005).
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Berger, Y. G. (2007) A jackknife variance estimator for unistage stratified samples with unequal probabilities. Biometrika 94, 953–964.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
VE.Jk.Tukey.Total.Hajek
VE.Jk.CBS.HT.Total.Hajek
VE.Jk.CBS.SYG.Total.Hajek
VE.Jk.EB.SW2.Total.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #Computes the var. est. of the Hajek total point estimator using y1 VE.Jk.B.Total.Hajek(y1[s==1], pik.U[s==1], N) #Computes the var. est. of the Hajek total point estimator using y2 VE.Jk.B.Total.Hajek(y2[s==1], pik.U[s==1], N)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #Computes the var. est. of the Hajek total point estimator using y1 VE.Jk.B.Total.Hajek(y1[s==1], pik.U[s==1], N) #Computes the var. est. of the Hajek total point estimator using y2 VE.Jk.B.Total.Hajek(y2[s==1], pik.U[s==1], N)
Computes the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator for the estimator of a correlation coefficient of two variables using the Hajek (1971) point estimator. It uses the Horvitz-Thompson (1952) variance form.
VE.Jk.CBS.HT.Corr.Hajek(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VE.Jk.CBS.HT.Corr.Hajek(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
For the population correlation coefficient of two variables and
:
the point estimator of , assuming that
is unknown (see Sarndal et al., 1992, Sec. 5.9), is:
where is the Hajek (1971) point estimator of the population mean
,
and with
denoting the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator (implemented by the current function):
where
with
and where has the same functional form as
but omitting the
-th element from the sample
.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Campbell, C. (1980) A different view of finite population estimation. Proceedings of the Survey Research Methods Section of the American Statistical Association, 319–324.
Berger, Y. G. and Skinner, C. J. (2005) A jackknife variance estimator for unequal probability sampling. Journal of the Royal Statistical Society B, 67, 79–89.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
VE.Jk.Tukey.Corr.Hajek
VE.Jk.CBS.SYG.Corr.Hajek
VE.Jk.B.Corr.Hajek
VE.Jk.EB.SW2.Corr.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the corr. coeff. point estimator using y1 VE.Jk.CBS.HT.Corr.Hajek(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the corr. coeff. point estimator using y2 VE.Jk.CBS.HT.Corr.Hajek(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the corr. coeff. point estimator using y1 VE.Jk.CBS.HT.Corr.Hajek(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the corr. coeff. point estimator using y2 VE.Jk.CBS.HT.Corr.Hajek(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
Computes the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator for the Hajek estimator of a mean. It uses the Horvitz-Thompson (1952) variance form.
VE.Jk.CBS.HT.Mean.Hajek(VecY.s, VecPk.s, MatPkl.s)
VE.Jk.CBS.HT.Mean.Hajek(VecY.s, VecPk.s, MatPkl.s)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
For the population mean of the variable :
the approximately unbiased Hajek (1971) estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator (implemented by the current function):
where
with
and
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Campbell, C. (1980) A different view of finite population estimation. Proceedings of the Survey Research Methods Section of the American Statistical Association, 319–324.
Berger, Y. G. and Skinner, C. J. (2005) A jackknife variance estimator for unequal probability sampling. Journal of the Royal Statistical Society B, 67, 79–89.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
VE.Jk.Tukey.Mean.Hajek
VE.Jk.CBS.SYG.Mean.Hajek
VE.Jk.B.Mean.Hajek
VE.Jk.EB.SW2.Mean.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek mean point estimator using y1 VE.Jk.CBS.HT.Mean.Hajek(y1[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the Hajek mean point estimator using y2 VE.Jk.CBS.HT.Mean.Hajek(y2[s==1], pik.U[s==1], pikl.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek mean point estimator using y1 VE.Jk.CBS.HT.Mean.Hajek(y1[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the Hajek mean point estimator using y2 VE.Jk.CBS.HT.Mean.Hajek(y2[s==1], pik.U[s==1], pikl.s)
Computes the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator for the estimator of a ratio of two totals/means. It uses the Horvitz-Thompson (1952) variance form.
VE.Jk.CBS.HT.Ratio(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VE.Jk.CBS.HT.Ratio(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VecY.s |
vector of the numerator variable of interest; its length is equal to |
VecX.s |
vector of the denominator variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
For the population ratio of two totals/means of the variables and
:
the ratio estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator (implemented by the current function):
where
with
and
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Campbell, C. (1980) A different view of finite population estimation. Proceedings of the Survey Research Methods Section of the American Statistical Association, 319–324.
Berger, Y. G. and Skinner, C. J. (2005) A jackknife variance estimator for unequal probability sampling. Journal of the Royal Statistical Society B, 67, 79–89.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
VE.Lin.HT.Ratio
VE.Lin.SYG.Ratio
VE.Jk.Tukey.Ratio
VE.Jk.CBS.SYG.Ratio
VE.Jk.B.Ratio
VE.Jk.EB.SW2.Ratio
VE.EB.HT.Ratio
VE.EB.SYG.Ratio
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the ratio point estimator using y1 VE.Jk.CBS.HT.Ratio(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the ratio point estimator using y2 VE.Jk.CBS.HT.Ratio(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the ratio point estimator using y1 VE.Jk.CBS.HT.Ratio(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the ratio point estimator using y2 VE.Jk.CBS.HT.Ratio(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
Computes the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator for the estimator of the regression coefficient using the Hajek (1971) point estimator. It uses the Horvitz-Thompson (1952) variance form.
VE.Jk.CBS.HT.RegCo.Hajek(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VE.Jk.CBS.HT.RegCo.Hajek(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
From Linear Regression Analysis, for an imposed population model
the population regression coefficient , assuming that the population size
is unknown (see Sarndal et al., 1992, Sec. 5.10), can be estimated by:
where and
are the Hajek (1971) point estimators of the population means
and
, respectively,
and with
denoting the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator (implemented by the current function):
where
with
and where has the same functional form as
but omitting the
-th element from the sample
.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Campbell, C. (1980) A different view of finite population estimation. Proceedings of the Survey Research Methods Section of the American Statistical Association, 319–324.
Berger, Y. G. and Skinner, C. J. (2005) A jackknife variance estimator for unequal probability sampling. Journal of the Royal Statistical Society B, 67, 79–89.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
VE.Jk.CBS.HT.RegCoI.Hajek
VE.Jk.Tukey.RegCo.Hajek
VE.Jk.CBS.SYG.RegCo.Hajek
VE.Jk.B.RegCo.Hajek
VE.Jk.EB.SW2.RegCo.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the regression coeff. point estimator using y1 VE.Jk.CBS.HT.RegCo.Hajek(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the regression coeff. point estimator using y2 VE.Jk.CBS.HT.RegCo.Hajek(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the regression coeff. point estimator using y1 VE.Jk.CBS.HT.RegCo.Hajek(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the regression coeff. point estimator using y2 VE.Jk.CBS.HT.RegCo.Hajek(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
Computes the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator for the estimator of the intercept regression coefficient using the Hajek (1971) point estimator. It uses the Horvitz-Thompson (1952) variance form.
VE.Jk.CBS.HT.RegCoI.Hajek(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VE.Jk.CBS.HT.RegCoI.Hajek(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
From Linear Regression Analysis, for an imposed population model
the population intercept regression coefficient , assuming that the population size
is unknown (see Sarndal et al., 1992, Sec. 5.10), can be estimated by:
where and
are the Hajek (1971) point estimators of the population means
and
, respectively,
and with
denoting the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator (implemented by the current function):
where
with
and where has the same functional form as
but omitting the
-th element from the sample
.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Campbell, C. (1980) A different view of finite population estimation. Proceedings of the Survey Research Methods Section of the American Statistical Association, 319–324.
Berger, Y. G. and Skinner, C. J. (2005) A jackknife variance estimator for unequal probability sampling. Journal of the Royal Statistical Society B, 67, 79–89.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
VE.Jk.CBS.HT.RegCo.Hajek
VE.Jk.Tukey.RegCoI.Hajek
VE.Jk.CBS.SYG.RegCoI.Hajek
VE.Jk.B.RegCoI.Hajek
VE.Jk.EB.SW2.RegCoI.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the intercept reg. coeff. point estimator using y1 VE.Jk.CBS.HT.RegCoI.Hajek(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the intercept reg. coeff. point estimator using y2 VE.Jk.CBS.HT.RegCoI.Hajek(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the intercept reg. coeff. point estimator using y1 VE.Jk.CBS.HT.RegCoI.Hajek(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the intercept reg. coeff. point estimator using y2 VE.Jk.CBS.HT.RegCoI.Hajek(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
Computes the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator for the Hajek estimator of a total. It uses the Horvitz-Thompson (1952) variance form.
VE.Jk.CBS.HT.Total.Hajek(VecY.s, VecPk.s, MatPkl.s, N)
VE.Jk.CBS.HT.Total.Hajek(VecY.s, VecPk.s, MatPkl.s, N)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. |
For the population total of the variable :
the approximately unbiased Hajek (1971) estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator (implemented by the current function):
where
with
and
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Campbell, C. (1980) A different view of finite population estimation. Proceedings of the Survey Research Methods Section of the American Statistical Association, 319–324.
Berger, Y. G. and Skinner, C. J. (2005) A jackknife variance estimator for unequal probability sampling. Journal of the Royal Statistical Society B, 67, 79–89.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
VE.Jk.Tukey.Total.Hajek
VE.Jk.CBS.SYG.Total.Hajek
VE.Jk.B.Total.Hajek
VE.Jk.EB.SW2.Total.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek total point estimator using y1 VE.Jk.CBS.HT.Total.Hajek(y1[s==1], pik.U[s==1], pikl.s, N) #Computes the var. est. of the Hajek total point estimator using y2 VE.Jk.CBS.HT.Total.Hajek(y2[s==1], pik.U[s==1], pikl.s, N)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek total point estimator using y1 VE.Jk.CBS.HT.Total.Hajek(y1[s==1], pik.U[s==1], pikl.s, N) #Computes the var. est. of the Hajek total point estimator using y2 VE.Jk.CBS.HT.Total.Hajek(y2[s==1], pik.U[s==1], pikl.s, N)
Computes the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator for the estimator of a correlation coefficient of two variables using the Hajek (1971) point estimator. It uses the Sen (1953); Yates-Grundy(1953) variance form.
VE.Jk.CBS.SYG.Corr.Hajek(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VE.Jk.CBS.SYG.Corr.Hajek(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
For the population correlation coefficient of two variables and
:
the point estimator of , assuming that
is unknown (see Sarndal et al., 1992, Sec. 5.9), is:
where is the Hajek (1971) point estimator of the population mean
,
and with
denoting the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator (implemented by the current function):
where
with
and where has the same functional form as
but omitting the
-th element from the sample
.
The Sen-Yates-Grundy form for the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator is proposed in Escobar-Berger (2013) under less-restrictive regularity conditions.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Campbell, C. (1980) A different view of finite population estimation. Proceedings of the Survey Research Methods Section of the American Statistical Association, 319–324.
Berger, Y. G. and Skinner, C. J. (2005) A jackknife variance estimator for unequal probability sampling. Journal of the Royal Statistical Society B, 67, 79–89.
Escobar, E. L. and Berger, Y. G. (2013) A jackknife variance estimator for self-weighted two-stage samples. Statistica Sinica, 23, 595–613.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
Sen, A. R. (1953) On the estimate of the variance in sampling with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 5, 119–127.
Yates, F. and Grundy, P. M. (1953) Selection without replacement from within strata with probability proportional to size. Journal of the Royal Statistical Society B, 15, 253–261.
VE.Jk.Tukey.Corr.Hajek
VE.Jk.CBS.HT.Corr.Hajek
VE.Jk.B.Corr.Hajek
VE.Jk.EB.SW2.Corr.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the corr. coeff. point estimator using y1 VE.Jk.CBS.SYG.Corr.Hajek(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the corr. coeff. point estimator using y2 VE.Jk.CBS.SYG.Corr.Hajek(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the corr. coeff. point estimator using y1 VE.Jk.CBS.SYG.Corr.Hajek(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the corr. coeff. point estimator using y2 VE.Jk.CBS.SYG.Corr.Hajek(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
Computes the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator for the Hajek estimator of a mean. It uses the Sen (1953); Yates-Grundy(1953) variance form.
VE.Jk.CBS.SYG.Mean.Hajek(VecY.s, VecPk.s, MatPkl.s)
VE.Jk.CBS.SYG.Mean.Hajek(VecY.s, VecPk.s, MatPkl.s)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
For the population mean of the variable :
the approximately unbiased Hajek (1971) estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator (implemented by the current function):
where
with
and
The Sen-Yates-Grundy form for the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator is proposed in Escobar-Berger (2013) under less-restrictive regularity conditions.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Campbell, C. (1980) A different view of finite population estimation. Proceedings of the Survey Research Methods Section of the American Statistical Association, 319–324.
Berger, Y. G. and Skinner, C. J. (2005) A jackknife variance estimator for unequal probability sampling. Journal of the Royal Statistical Society B, 67, 79–89.
Escobar, E. L. and Berger, Y. G. (2013) A jackknife variance estimator for self-weighted two-stage samples. Statistica Sinica, 23, 595–613.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sen, A. R. (1953) On the estimate of the variance in sampling with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 5, 119–127.
Yates, F. and Grundy, P. M. (1953) Selection without replacement from within strata with probability proportional to size. Journal of the Royal Statistical Society B, 15, 253–261.
VE.Jk.Tukey.Mean.Hajek
VE.Jk.CBS.HT.Mean.Hajek
VE.Jk.B.Mean.Hajek
VE.Jk.EB.SW2.Mean.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek mean point estimator using y1 VE.Jk.CBS.SYG.Mean.Hajek(y1[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the Hajek mean point estimator using y2 VE.Jk.CBS.SYG.Mean.Hajek(y2[s==1], pik.U[s==1], pikl.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek mean point estimator using y1 VE.Jk.CBS.SYG.Mean.Hajek(y1[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the Hajek mean point estimator using y2 VE.Jk.CBS.SYG.Mean.Hajek(y2[s==1], pik.U[s==1], pikl.s)
Computes the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator for the estimator of a ratio of two totals/means. It uses the Sen (1953); Yates-Grundy(1953) variance form.
VE.Jk.CBS.SYG.Ratio(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VE.Jk.CBS.SYG.Ratio(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VecY.s |
vector of the numerator variable of interest; its length is equal to |
VecX.s |
vector of the denominator variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
For the population ratio of two totals/means of the variables and
:
the ratio estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator (implemented by the current function):
where
with
and
The Sen-Yates-Grundy form for the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator is proposed in Escobar-Berger (2013) under less-restrictive regularity conditions.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Campbell, C. (1980) A different view of finite population estimation. Proceedings of the Survey Research Methods Section of the American Statistical Association, 319–324.
Berger, Y. G. and Skinner, C. J. (2005) A jackknife variance estimator for unequal probability sampling. Journal of the Royal Statistical Society B, 67, 79–89.
Escobar, E. L. and Berger, Y. G. (2013) A jackknife variance estimator for self-weighted two-stage samples. Statistica Sinica, 23, 595–613.
Sen, A. R. (1953) On the estimate of the variance in sampling with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 5, 119–127.
Yates, F. and Grundy, P. M. (1953) Selection without replacement from within strata with probability proportional to size. Journal of the Royal Statistical Society B, 15, 253–261.
VE.Lin.HT.Ratio
VE.Lin.SYG.Ratio
VE.Jk.Tukey.Ratio
VE.Jk.CBS.HT.Ratio
VE.Jk.B.Ratio
VE.Jk.EB.SW2.Ratio
VE.EB.HT.Ratio
VE.EB.SYG.Ratio
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used for y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the ratio point estimator using y1 VE.Jk.CBS.SYG.Ratio(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the ratio point estimator using y2 VE.Jk.CBS.SYG.Ratio(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used for y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the ratio point estimator using y1 VE.Jk.CBS.SYG.Ratio(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the ratio point estimator using y2 VE.Jk.CBS.SYG.Ratio(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
Computes the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator for the estimator of the regression coefficient using the Hajek (1971) point estimator. It uses the Sen (1953); Yates-Grundy(1953) variance form.
VE.Jk.CBS.SYG.RegCo.Hajek(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VE.Jk.CBS.SYG.RegCo.Hajek(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
From Linear Regression Analysis, for an imposed population model
the population regression coefficient , assuming that the population size
is unknown (see Sarndal et al., 1992, Sec. 5.10), can be estimated by:
where and
are the Hajek (1971) point estimators of the population means
and
, respectively,
and with
denoting the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator (implemented by the current function):
where
with
and where has the same functional form as
but omitting the
-th element from the sample
.
The Sen-Yates-Grundy form for the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator is proposed in Escobar-Berger (2013) under less-restrictive regularity conditions.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Campbell, C. (1980) A different view of finite population estimation. Proceedings of the Survey Research Methods Section of the American Statistical Association, 319–324.
Berger, Y. G. and Skinner, C. J. (2005) A jackknife variance estimator for unequal probability sampling. Journal of the Royal Statistical Society B, 67, 79–89.
Escobar, E. L. and Berger, Y. G. (2013) A jackknife variance estimator for self-weighted two-stage samples. Statistica Sinica, 23, 595–613.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
Sen, A. R. (1953) On the estimate of the variance in sampling with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 5, 119–127.
Yates, F. and Grundy, P. M. (1953) Selection without replacement from within strata with probability proportional to size. Journal of the Royal Statistical Society B, 15, 253–261.
VE.Jk.CBS.SYG.RegCoI.Hajek
VE.Jk.Tukey.RegCo.Hajek
VE.Jk.CBS.HT.RegCo.Hajek
VE.Jk.B.RegCo.Hajek
VE.Jk.EB.SW2.RegCo.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the regression coeff. point estimator using y1 VE.Jk.CBS.SYG.RegCo.Hajek(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the regression coeff. point estimator using y2 VE.Jk.CBS.SYG.RegCo.Hajek(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the regression coeff. point estimator using y1 VE.Jk.CBS.SYG.RegCo.Hajek(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the regression coeff. point estimator using y2 VE.Jk.CBS.SYG.RegCo.Hajek(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
Computes the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator for the estimator of the intercept regression coefficient using the Hajek (1971) point estimator. It uses the Sen (1953); Yates-Grundy(1953) variance form.
VE.Jk.CBS.SYG.RegCoI.Hajek(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VE.Jk.CBS.SYG.RegCoI.Hajek(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
From Linear Regression Analysis, for an imposed population model
the population intercept regression coefficient , assuming that the population size
is unknown (see Sarndal et al., 1992, Sec. 5.10), can be estimated by:
where and
are the Hajek (1971) point estimators of the population means
and
, respectively,
and with
denoting the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator (implemented by the current function):
where
with
and where has the same functional form as
but omitting the
-th element from the sample
.
The Sen-Yates-Grundy form for the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator is proposed in Escobar-Berger (2013) under less-restrictive regularity conditions.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Campbell, C. (1980) A different view of finite population estimation. Proceedings of the Survey Research Methods Section of the American Statistical Association, 319–324.
Berger, Y. G. and Skinner, C. J. (2005) A jackknife variance estimator for unequal probability sampling. Journal of the Royal Statistical Society B, 67, 79–89.
Escobar, E. L. and Berger, Y. G. (2013) A jackknife variance estimator for self-weighted two-stage samples. Statistica Sinica, 23, 595–613.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
Sen, A. R. (1953) On the estimate of the variance in sampling with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 5, 119–127.
Yates, F. and Grundy, P. M. (1953) Selection without replacement from within strata with probability proportional to size. Journal of the Royal Statistical Society B, 15, 253–261.
VE.Jk.CBS.SYG.RegCo.Hajek
VE.Jk.Tukey.RegCoI.Hajek
VE.Jk.CBS.HT.RegCoI.Hajek
VE.Jk.B.RegCoI.Hajek
VE.Jk.EB.SW2.RegCoI.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the intercept reg. coeff. point estimator using y1 VE.Jk.CBS.SYG.RegCoI.Hajek(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the intercept reg. coeff. point estimator using y2 VE.Jk.CBS.SYG.RegCoI.Hajek(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the intercept reg. coeff. point estimator using y1 VE.Jk.CBS.SYG.RegCoI.Hajek(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the intercept reg. coeff. point estimator using y2 VE.Jk.CBS.SYG.RegCoI.Hajek(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
Computes the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator for the Hajek estimator of a total. It uses the Sen (1953); Yates-Grundy(1953) variance form.
VE.Jk.CBS.SYG.Total.Hajek(VecY.s, VecPk.s, MatPkl.s, N)
VE.Jk.CBS.SYG.Total.Hajek(VecY.s, VecPk.s, MatPkl.s, N)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. |
For the population total of the variable :
the approximately unbiased Hajek (1971) estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator (implemented by the current function):
where
with
and
The Sen-Yates-Grundy form for the Campbell(1980); Berger-Skinner(2005) unequal probability jackknife variance estimator is proposed in Escobar-Berger (2013) under less-restrictive regularity conditions.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Campbell, C. (1980) A different view of finite population estimation. Proceedings of the Survey Research Methods Section of the American Statistical Association, 319–324.
Berger, Y. G. and Skinner, C. J. (2005) A jackknife variance estimator for unequal probability sampling. Journal of the Royal Statistical Society B, 67, 79–89.
Escobar, E. L. and Berger, Y. G. (2013) A jackknife variance estimator for self-weighted two-stage samples. Statistica Sinica, 23, 595–613.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sen, A. R. (1953) On the estimate of the variance in sampling with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 5, 119–127.
Yates, F. and Grundy, P. M. (1953) Selection without replacement from within strata with probability proportional to size. Journal of the Royal Statistical Society B, 15, 253–261.
VE.Jk.Tukey.Total.Hajek
VE.Jk.CBS.HT.Total.Hajek
VE.Jk.B.Total.Hajek
VE.Jk.EB.SW2.Total.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek total point estimator using y1 VE.Jk.CBS.SYG.Total.Hajek(y1[s==1], pik.U[s==1], pikl.s, N) #Computes the var. est. of the Hajek total point estimator using y2 VE.Jk.CBS.SYG.Total.Hajek(y2[s==1], pik.U[s==1], pikl.s, N)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the Hajek total point estimator using y1 VE.Jk.CBS.SYG.Total.Hajek(y1[s==1], pik.U[s==1], pikl.s, N) #Computes the var. est. of the Hajek total point estimator using y2 VE.Jk.CBS.SYG.Total.Hajek(y2[s==1], pik.U[s==1], pikl.s, N)
Computes the self-weighted two-stage sampling Escobar-Berger (2013) jackknife variance estimator for the estimator of a correlation coefficient of two variables using the Hajek (1971) point estimator.
VE.Jk.EB.SW2.Corr.Hajek(VecY.s, VecX.s, VecPk.s, nII, VecPi.s, VecCluLab.s, VecCluSize.s)
VE.Jk.EB.SW2.Corr.Hajek(VecY.s, VecX.s, VecPk.s, nII, VecPi.s, VecCluLab.s, VecCluSize.s)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the elements' first-order inclusion probabilities; its length is equal to |
nII |
the second stage sample size, i.e., the fixed number of ultimate sampling units selected within each cluster. Its size must be less than or equal to the minimum cluster size in the sample. |
VecPi.s |
vector of the clusters' first-order inclusion probabilities; its length is equal to |
VecCluLab.s |
vector of the clusters' labels for the elements; its length is equal to |
VecCluSize.s |
vector of the clusters' sizes; its length is equal to |
For the population correlation coefficient of two variables and
:
the point estimator of , assuming that
is unknown (see Sarndal et al., 1992, Sec. 5.9), is:
where is the Hajek (1971) point estimator of the population mean
,
and with
denoting the inclusion probability of the
-th element in the sample
. If
is a self-weighted two-stage sample, the variance of
can be estimated by the Escobar-Berger (2013) jackknife variance estimator (implemented by the current function):
where ,
,
, with
denoting the sample elements from the
-th cluster,
is an indicator that takes the value
if the
-th observation is within the
-th cluster and
otherwise,
is the inclusion probability of the
-th cluster in the sample
,
is the size of the
-th cluster,
is the sample size within each cluster,
is the number of sampled clusters, and where
where and
have the same functional form as
but omitting the
-th cluster and the
-th element, respectively, from the sample
.
Note that this variance estimator implicitly utilises the Hajek (1964) approximations that are designed for large-entropy sampling designs, large samples, and large populations, i.e., care should be taken with highly-stratified samples, e.g. Berger (2005).
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Escobar, E. L. and Berger, Y. G. (2013) A jackknife variance estimator for self-weighted two-stage samples. Statistica Sinica, 23, 595–613.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
VE.Jk.Tukey.Corr.Hajek
VE.Jk.CBS.HT.Corr.Hajek
VE.Jk.CBS.SYG.Corr.Hajek
VE.Jk.B.Corr.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset s <- oaxaca$sSW_10_3 #Defines the sample to be used SampData <- oaxaca[s==1, ] #Defines the sample dataset nII <- 3 #Defines the 2nd stage fixed sample size CluLab.s <- SampData$IDDISTRI #Defines the clusters' labels CluSize.s <- SampData$SIZEDIST #Defines the clusters' sizes piIi.s <- (10 * CluSize.s / 570) #Reconstructs clusters' 1st order incl. probs. pik.s <- piIi.s * (nII/CluSize.s) #Reconstructs elements' 1st order incl. probs. y1.s <- SampData$POP10 #Defines the variable y1 y2.s <- SampData$POPMAL10 #Defines the variable y2 x.s <- SampData$HOMES10 #Defines the variable x #Computes the var. est. of the corr. coeff. point estimator using y1 VE.Jk.EB.SW2.Corr.Hajek(y1.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s) #Computes the var. est. of the corr. coeff. point estimator using y2 VE.Jk.EB.SW2.Corr.Hajek(y2.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset s <- oaxaca$sSW_10_3 #Defines the sample to be used SampData <- oaxaca[s==1, ] #Defines the sample dataset nII <- 3 #Defines the 2nd stage fixed sample size CluLab.s <- SampData$IDDISTRI #Defines the clusters' labels CluSize.s <- SampData$SIZEDIST #Defines the clusters' sizes piIi.s <- (10 * CluSize.s / 570) #Reconstructs clusters' 1st order incl. probs. pik.s <- piIi.s * (nII/CluSize.s) #Reconstructs elements' 1st order incl. probs. y1.s <- SampData$POP10 #Defines the variable y1 y2.s <- SampData$POPMAL10 #Defines the variable y2 x.s <- SampData$HOMES10 #Defines the variable x #Computes the var. est. of the corr. coeff. point estimator using y1 VE.Jk.EB.SW2.Corr.Hajek(y1.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s) #Computes the var. est. of the corr. coeff. point estimator using y2 VE.Jk.EB.SW2.Corr.Hajek(y2.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s)
Computes the self-weighted two-stage sampling Escobar-Berger (2013) jackknife variance estimator for the Hajek estimator of a mean.
VE.Jk.EB.SW2.Mean.Hajek(VecY.s, VecPk.s, nII, VecPi.s, VecCluLab.s, VecCluSize.s)
VE.Jk.EB.SW2.Mean.Hajek(VecY.s, VecPk.s, nII, VecPi.s, VecCluLab.s, VecCluSize.s)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the elements' first-order inclusion probabilities; its length is equal to |
nII |
the second stage sample size, i.e., the fixed number of ultimate sampling units selected within each cluster. Its size must be less than or equal to the minimum cluster size in the sample. |
VecPi.s |
vector of the clusters' first-order inclusion probabilities; its length is equal to |
VecCluLab.s |
vector of the clusters' labels for the elements; its length is equal to |
VecCluSize.s |
vector of the clusters' sizes; its length is equal to |
For the population mean of the variable :
the approximately unbiased Hajek (1971) estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. If
is a self-weighted two-stage sample, the variance of
can be estimated by the Escobar-Berger (2013) jackknife variance estimator (implemented by the current function):
where ,
,
, with
denoting the sample elements from the
-th cluster,
is an indicator that takes the value
if the
-th observation is within the
-th cluster and
otherwise,
is the inclusion probability of the
-th cluster in the sample
,
is the size of the
-th cluster,
is the sample size within each cluster,
is the number of sampled clusters, and where
where and
have the same functional form as
but omitting the
-th cluster and the
-th element, respectively, from the sample
.
Note that this variance estimator implicitly utilises the Hajek (1964) approximations that are designed for large-entropy sampling designs, large samples, and large populations, i.e., care should be taken with highly-stratified samples, e.g. Berger (2005).
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Escobar, E. L. and Berger, Y. G. (2013) A jackknife variance estimator for self-weighted two-stage samples. Statistica Sinica, 23, 595–613.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
VE.Jk.Tukey.Mean.Hajek
VE.Jk.CBS.HT.Mean.Hajek
VE.Jk.CBS.SYG.Mean.Hajek
VE.Jk.B.Mean.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset s <- oaxaca$sSW_10_3 #Defines the sample to be used SampData <- oaxaca[s==1, ] #Defines the sample dataset nII <- 3 #Defines the 2nd stage fixed sample size CluLab.s <- SampData$IDDISTRI #Defines the clusters' labels CluSize.s <- SampData$SIZEDIST #Defines the clusters' sizes piIi.s <- (10 * CluSize.s / 570) #Reconstructs clusters' 1st order incl. probs. pik.s <- piIi.s * (nII/CluSize.s) #Reconstructs elements' 1st order incl. probs. y1.s <- SampData$POP10 #Defines the variable of interest y1 y2.s <- SampData$POPMAL10 #Defines the variable of interest y2 #Computes the var. est. of the Hajek mean point estimator using y1 VE.Jk.EB.SW2.Mean.Hajek(y1.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s) #Computes the var. est. of the Hajek mean point estimator using y2 VE.Jk.EB.SW2.Mean.Hajek(y2.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset s <- oaxaca$sSW_10_3 #Defines the sample to be used SampData <- oaxaca[s==1, ] #Defines the sample dataset nII <- 3 #Defines the 2nd stage fixed sample size CluLab.s <- SampData$IDDISTRI #Defines the clusters' labels CluSize.s <- SampData$SIZEDIST #Defines the clusters' sizes piIi.s <- (10 * CluSize.s / 570) #Reconstructs clusters' 1st order incl. probs. pik.s <- piIi.s * (nII/CluSize.s) #Reconstructs elements' 1st order incl. probs. y1.s <- SampData$POP10 #Defines the variable of interest y1 y2.s <- SampData$POPMAL10 #Defines the variable of interest y2 #Computes the var. est. of the Hajek mean point estimator using y1 VE.Jk.EB.SW2.Mean.Hajek(y1.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s) #Computes the var. est. of the Hajek mean point estimator using y2 VE.Jk.EB.SW2.Mean.Hajek(y2.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s)
Computes the self-weighted two-stage sampling Escobar-Berger (2013) jackknife variance estimator for the estimator of a ratio of two totals/means.
VE.Jk.EB.SW2.Ratio(VecY.s, VecX.s, VecPk.s, nII, VecPi.s, VecCluLab.s, VecCluSize.s)
VE.Jk.EB.SW2.Ratio(VecY.s, VecX.s, VecPk.s, nII, VecPi.s, VecCluLab.s, VecCluSize.s)
VecY.s |
vector of the numerator variable of interest; its length is equal to |
VecX.s |
vector of the denominator variable of interest; its length is equal to |
VecPk.s |
vector of the elements' first-order inclusion probabilities; its length is equal to |
nII |
the second stage sample size, i.e., the fixed number of ultimate sampling units selected within each cluster. Its size must be less than or equal to the minimum cluster size in the sample. |
VecPi.s |
vector of the clusters' first-order inclusion probabilities; its length is equal to |
VecCluLab.s |
vector of the clusters' labels for the elements; its length is equal to |
VecCluSize.s |
vector of the clusters' sizes; its length is equal to |
For the population ratio of two totals/means of the variables and
:
the ratio estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. If
is a self-weighted two-stage sample, the variance of
can be estimated by the Escobar-Berger (2013) jackknife variance estimator (implemented by the current function):
where ,
,
, with
denoting the sample elements from the
-th cluster,
is an indicator that takes the value
if the
-th observation is within the
-th cluster and
otherwise,
is the inclusion probability of the
-th cluster in the sample
,
is the size of the
-th cluster,
is the sample size within each cluster,
is the number of sampled clusters, and where
where and
have the same functional form as
but omitting the
-th cluster and the
-th element, respectively, from the sample
.
Note that this variance estimator implicitly utilises the Hajek (1964) approximations that are designed for large-entropy sampling designs, large samples, and large populations, i.e., care should be taken with highly-stratified samples, e.g. Berger (2005).
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Escobar, E. L. and Berger, Y. G. (2013) A jackknife variance estimator for self-weighted two-stage samples. Statistica Sinica, 23, 595–613.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
VE.Jk.Tukey.Ratio
VE.Jk.CBS.HT.Ratio
VE.Jk.CBS.SYG.Ratio
VE.Jk.B.Ratio
VE.EB.HT.Ratio
VE.EB.SYG.Ratio
data(oaxaca) #Loads the Oaxaca municipalities dataset s <- oaxaca$sSW_10_3 #Defines the sample to be used SampData <- oaxaca[s==1, ] #Defines the sample dataset nII <- 3 #Defines the 2nd stage fixed sample size CluLab.s <- SampData$IDDISTRI #Defines the clusters' labels CluSize.s <- SampData$SIZEDIST #Defines the clusters' sizes piIi.s <- (10 * CluSize.s / 570) #Reconstructs clusters' 1st order incl. probs. pik.s <- piIi.s * (nII/CluSize.s) #Reconstructs elements' 1st order incl. probs. y1.s <- SampData$POP10 #Defines the numerator variable y1 y2.s <- SampData$POPMAL10 #Defines the numerator variable y2 x.s <- SampData$HOMES10 #Defines the denominator variable x #Computes the var. est. of the ratio point estimator using y1 VE.Jk.EB.SW2.Ratio(y1.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s) #Computes the var. est. of the ratio point estimator using y2 VE.Jk.EB.SW2.Ratio(y2.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset s <- oaxaca$sSW_10_3 #Defines the sample to be used SampData <- oaxaca[s==1, ] #Defines the sample dataset nII <- 3 #Defines the 2nd stage fixed sample size CluLab.s <- SampData$IDDISTRI #Defines the clusters' labels CluSize.s <- SampData$SIZEDIST #Defines the clusters' sizes piIi.s <- (10 * CluSize.s / 570) #Reconstructs clusters' 1st order incl. probs. pik.s <- piIi.s * (nII/CluSize.s) #Reconstructs elements' 1st order incl. probs. y1.s <- SampData$POP10 #Defines the numerator variable y1 y2.s <- SampData$POPMAL10 #Defines the numerator variable y2 x.s <- SampData$HOMES10 #Defines the denominator variable x #Computes the var. est. of the ratio point estimator using y1 VE.Jk.EB.SW2.Ratio(y1.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s) #Computes the var. est. of the ratio point estimator using y2 VE.Jk.EB.SW2.Ratio(y2.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s)
Computes the self-weighted two-stage sampling Escobar-Berger (2013) jackknife variance estimator for the estimator of the regression coefficient using the Hajek (1971) point estimator.
VE.Jk.EB.SW2.RegCo.Hajek(VecY.s, VecX.s, VecPk.s, nII, VecPi.s, VecCluLab.s, VecCluSize.s)
VE.Jk.EB.SW2.RegCo.Hajek(VecY.s, VecX.s, VecPk.s, nII, VecPi.s, VecCluLab.s, VecCluSize.s)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the elements' first-order inclusion probabilities; its length is equal to |
nII |
the second stage sample size, i.e. the fixed number of ultimate sampling units that were selected within each cluster. Its size must be less than or equal to the minimum cluster size in the sample. |
VecPi.s |
vector of the clusters' first-order inclusion probabilities; its length is equal to |
VecCluLab.s |
vector of the clusters' labels for the elements; its length is equal to |
VecCluSize.s |
vector of the clusters' sizes; its length is equal to |
From Linear Regression Analysis, for an imposed population model
the population regression coefficient , assuming that the population size
is unknown (see Sarndal et al., 1992, Sec. 5.10), can be estimated by:
where and
are the Hajek (1971) point estimators of the population means
and
, respectively,
and with
denoting the inclusion probability of the
-th element in the sample
. If
is a self-weighted two-stage sample, the variance of
can be estimated by the Escobar-Berger (2013) jackknife variance estimator (implemented by the current function):
where ,
,
, with
denoting the sample elements from the
-th cluster,
is an indicator that takes the value
if the
-th observation is within the
-th cluster and
otherwise,
is the inclusion probability of the
-th cluster in the sample
,
is the size of the
-th cluster,
is the sample size within each cluster,
is the number of sampled clusters, and where
where and
have the same functional form as
but omitting the
-th cluster and the
-th element, respectively, from the sample
.
Note that this variance estimator implicitly utilises the Hajek (1964) approximations that are designed for large-entropy sampling designs, large samples, and large populations, i.e., care should be taken with highly-stratified samples, e.g. Berger (2005).
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Escobar, E. L. and Berger, Y. G. (2013) A jackknife variance estimator for self-weighted two-stage samples. Statistica Sinica, 23, 595–613.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
VE.Jk.EB.SW2.RegCoI.Hajek
VE.Jk.Tukey.RegCo.Hajek
VE.Jk.CBS.HT.RegCo.Hajek
VE.Jk.CBS.SYG.RegCo.Hajek
VE.Jk.B.RegCo.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset s <- oaxaca$sSW_10_3 #Defines the sample to be used SampData <- oaxaca[s==1, ] #Defines the sample dataset nII <- 3 #Defines the 2nd stage fixed sample size CluLab.s <- SampData$IDDISTRI #Defines the clusters' labels CluSize.s <- SampData$SIZEDIST #Defines the clusters' sizes piIi.s <- (10 * CluSize.s / 570) #Reconstructs clusters' 1st order incl. probs. pik.s <- piIi.s * (nII/CluSize.s) #Reconstructs elements' 1st order incl. probs. y1.s <- SampData$POP10 #Defines the variable y1 y2.s <- SampData$POPMAL10 #Defines the variable y2 x.s <- SampData$HOMES10 #Defines the variable x #Computes the var. est. of the regression coeff. point estimator using y1 VE.Jk.EB.SW2.RegCo.Hajek(y1.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s) #Computes the var. est. of the regression coeff. point estimator using y2 VE.Jk.EB.SW2.RegCo.Hajek(y2.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset s <- oaxaca$sSW_10_3 #Defines the sample to be used SampData <- oaxaca[s==1, ] #Defines the sample dataset nII <- 3 #Defines the 2nd stage fixed sample size CluLab.s <- SampData$IDDISTRI #Defines the clusters' labels CluSize.s <- SampData$SIZEDIST #Defines the clusters' sizes piIi.s <- (10 * CluSize.s / 570) #Reconstructs clusters' 1st order incl. probs. pik.s <- piIi.s * (nII/CluSize.s) #Reconstructs elements' 1st order incl. probs. y1.s <- SampData$POP10 #Defines the variable y1 y2.s <- SampData$POPMAL10 #Defines the variable y2 x.s <- SampData$HOMES10 #Defines the variable x #Computes the var. est. of the regression coeff. point estimator using y1 VE.Jk.EB.SW2.RegCo.Hajek(y1.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s) #Computes the var. est. of the regression coeff. point estimator using y2 VE.Jk.EB.SW2.RegCo.Hajek(y2.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s)
Computes the self-weighted two-stage sampling Escobar-Berger (2013) jackknife variance estimator for the estimator of the intercept regression coefficient using the Hajek (1971) point estimator.
VE.Jk.EB.SW2.RegCoI.Hajek(VecY.s, VecX.s, VecPk.s, nII, VecPi.s, VecCluLab.s, VecCluSize.s)
VE.Jk.EB.SW2.RegCoI.Hajek(VecY.s, VecX.s, VecPk.s, nII, VecPi.s, VecCluLab.s, VecCluSize.s)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the elements' first-order inclusion probabilities; its length is equal to |
nII |
the second stage sample size, i.e. the fixed number of ultimate sampling units that were selected within each cluster. Its size must be less than or equal to the minimum cluster size in the sample. |
VecPi.s |
vector of the clusters' first-order inclusion probabilities; its length is equal to |
VecCluLab.s |
vector of the clusters' labels for the elements; its length is equal to |
VecCluSize.s |
vector of the clusters' sizes; its length is equal to |
From Linear Regression Analysis, for an imposed population model
the population intercept regression coefficient , assuming that the population size
is unknown (see Sarndal et al., 1992, Sec. 5.10), can be estimated by:
where and
are the Hajek (1971) point estimators of the population means
and
, respectively,
and with
denoting the inclusion probability of the
-th element in the sample
. If
is a self-weighted two-stage sample, the variance of
can be estimated by the Escobar-Berger (2013) jackknife variance estimator (implemented by the current function):
where ,
,
, with
denoting the sample elements from the
-th cluster,
is an indicator that takes the value
if the
-th observation is within the
-th cluster and
otherwise,
is the inclusion probability of the
-th cluster in the sample
,
is the size of the
-th cluster,
is the sample size within each cluster,
is the number of sampled clusters, and where
where and
have the same functional form as
but omitting the
-th cluster and the
-th element, respectively, from the sample
.
Note that this variance estimator implicitly utilises the Hajek (1964) approximations that are designed for large-entropy sampling designs, large samples, and large populations, i.e., care should be taken with highly-stratified samples, e.g. Berger (2005).
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Escobar, E. L. and Berger, Y. G. (2013) A jackknife variance estimator for self-weighted two-stage samples. Statistica Sinica, 23, 595–613.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
VE.Jk.EB.SW2.RegCo.Hajek
VE.Jk.Tukey.RegCoI.Hajek
VE.Jk.CBS.HT.RegCoI.Hajek
VE.Jk.CBS.SYG.RegCoI.Hajek
VE.Jk.B.RegCoI.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset s <- oaxaca$sSW_10_3 #Defines the sample to be used SampData <- oaxaca[s==1, ] #Defines the sample dataset nII <- 3 #Defines the 2nd stage fixed sample size CluLab.s <- SampData$IDDISTRI #Defines the clusters' labels CluSize.s <- SampData$SIZEDIST #Defines the clusters' sizes piIi.s <- (10 * CluSize.s / 570) #Reconstructs clusters' 1st order incl. probs. pik.s <- piIi.s * (nII/CluSize.s) #Reconstructs elements' 1st order incl. probs. y1.s <- SampData$POP10 #Defines the variable y1 y2.s <- SampData$POPMAL10 #Defines the variable y2 x.s <- SampData$HOMES10 #Defines the variable x #Computes the var. est. of the intercept reg. coeff. point estimator using y1 VE.Jk.EB.SW2.RegCoI.Hajek(y1.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s) #Computes the var. est. of the intercept reg. coeff. point estimator using y2 VE.Jk.EB.SW2.RegCoI.Hajek(y2.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset s <- oaxaca$sSW_10_3 #Defines the sample to be used SampData <- oaxaca[s==1, ] #Defines the sample dataset nII <- 3 #Defines the 2nd stage fixed sample size CluLab.s <- SampData$IDDISTRI #Defines the clusters' labels CluSize.s <- SampData$SIZEDIST #Defines the clusters' sizes piIi.s <- (10 * CluSize.s / 570) #Reconstructs clusters' 1st order incl. probs. pik.s <- piIi.s * (nII/CluSize.s) #Reconstructs elements' 1st order incl. probs. y1.s <- SampData$POP10 #Defines the variable y1 y2.s <- SampData$POPMAL10 #Defines the variable y2 x.s <- SampData$HOMES10 #Defines the variable x #Computes the var. est. of the intercept reg. coeff. point estimator using y1 VE.Jk.EB.SW2.RegCoI.Hajek(y1.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s) #Computes the var. est. of the intercept reg. coeff. point estimator using y2 VE.Jk.EB.SW2.RegCoI.Hajek(y2.s, x.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s)
Computes the self-weighted two-stage sampling Escobar-Berger (2013) jackknife variance estimator for the Hajek estimator of a total.
VE.Jk.EB.SW2.Total.Hajek(VecY.s, VecPk.s, nII, VecPi.s, VecCluLab.s, VecCluSize.s, N)
VE.Jk.EB.SW2.Total.Hajek(VecY.s, VecPk.s, nII, VecPi.s, VecCluLab.s, VecCluSize.s, N)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the elements' first-order inclusion probabilities; its length is equal to |
nII |
the second stage sample size, i.e. the fixed number of ultimate sampling units that were selected within each cluster. Its size must be less than or equal to the minimum cluster size in the sample. |
VecPi.s |
vector of the clusters' first-order inclusion probabilities; its length is equal to |
VecCluLab.s |
vector of the clusters' labels for the elements; its length is equal to |
VecCluSize.s |
vector of the clusters' sizes; its length is equal to |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. |
For the population total of the variable :
the approximately unbiased Hajek (1971) estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. If
is a self-weighted two-stage sample, the variance of
can be estimated by the Escobar-Berger (2013) jackknife variance estimator (implemented by the current function):
where ,
,
, with
denoting the sample elements from the
-th cluster,
is an indicator that takes the value
if the
-th observation is within the
-th cluster and
otherwise,
is the inclusion probability of the
-th cluster in the sample
,
is the size of the
-th cluster,
is the sample size within each cluster,
is the number of sampled clusters, and where
where and
have the same functional form as
but omitting the
-th cluster and the
-th element, respectively, from the sample
.
Note that this variance estimator implicitly utilises the Hajek (1964) approximations that are designed for large-entropy sampling designs, large samples, and large populations, i.e., care should be taken with highly-stratified samples, e.g. Berger (2005).
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Berger, Y. G. (2005) Variance estimation with highly stratified sampling designs with unequal probabilities. Australian & New Zealand Journal of Statistics, 47, 365–373.
Escobar, E. L. and Berger, Y. G. (2013) A jackknife variance estimator for self-weighted two-stage samples. Statistica Sinica, 23, 595–613.
Hajek, J. (1964) Asymptotic theory of rejective sampling with varying probabilities from a finite population. The Annals of Mathematical Statistics, 35, 4, 1491–1523.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
VE.Jk.Tukey.Total.Hajek
VE.Jk.CBS.HT.Total.Hajek
VE.Jk.CBS.SYG.Total.Hajek
VE.Jk.B.Total.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset s <- oaxaca$sSW_10_3 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size SampData <- oaxaca[s==1, ] #Defines the sample dataset nII <- 3 #Defines the 2nd stage fixed sample size CluLab.s <- SampData$IDDISTRI #Defines the clusters' labels CluSize.s <- SampData$SIZEDIST #Defines the clusters' sizes piIi.s <- (10 * CluSize.s / 570) #Reconstructs clusters' 1st order incl. probs. pik.s <- piIi.s * (nII/CluSize.s) #Reconstructs elements' 1st order incl. probs. y1.s <- SampData$POP10 #Defines the variable of interest y1 y2.s <- SampData$POPMAL10 #Defines the variable of interest y2 #Computes the var. est. of the Hajek total point estimator using y1 VE.Jk.EB.SW2.Total.Hajek(y1.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s, N) #Computes the var. est. of the Hajek total point estimator using y2 VE.Jk.EB.SW2.Total.Hajek(y2.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s, N)
data(oaxaca) #Loads the Oaxaca municipalities dataset s <- oaxaca$sSW_10_3 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size SampData <- oaxaca[s==1, ] #Defines the sample dataset nII <- 3 #Defines the 2nd stage fixed sample size CluLab.s <- SampData$IDDISTRI #Defines the clusters' labels CluSize.s <- SampData$SIZEDIST #Defines the clusters' sizes piIi.s <- (10 * CluSize.s / 570) #Reconstructs clusters' 1st order incl. probs. pik.s <- piIi.s * (nII/CluSize.s) #Reconstructs elements' 1st order incl. probs. y1.s <- SampData$POP10 #Defines the variable of interest y1 y2.s <- SampData$POPMAL10 #Defines the variable of interest y2 #Computes the var. est. of the Hajek total point estimator using y1 VE.Jk.EB.SW2.Total.Hajek(y1.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s, N) #Computes the var. est. of the Hajek total point estimator using y2 VE.Jk.EB.SW2.Total.Hajek(y2.s, pik.s, nII, piIi.s, CluLab.s, CluSize.s, N)
Computes the Quenouille(1956); Tukey (1958) jackknife variance estimator for the estimator of a correlation coefficient of two variables using the Hajek (1971) point estimator.
VE.Jk.Tukey.Corr.Hajek(VecY.s, VecX.s, VecPk.s, N, FPC= TRUE)
VE.Jk.Tukey.Corr.Hajek(VecY.s, VecX.s, VecPk.s, N, FPC= TRUE)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. This information is utilised for the finite population correction only, see |
FPC |
logical value. If an ad hoc finite population correction |
For the population correlation coefficient of two variables and
:
the point estimator of , assuming that
is unknown (see Sarndal et al., 1992, Sec. 5.9), is:
where is the Hajek (1971) point estimator of the population mean
,
and with
denoting the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Quenouille(1956); Tukey (1958) jackknife variance estimator (implemented by the current function):
where has the same functional form as
but omitting the
-th element from the sample
.
Note that we are implementing the Tukey (1958) jackknife variance estimator using the ‘ad hoc’ finite population correction
(see Shao and Tu, 1995; Wolter, 2007). If
FPC=FALSE
then the term is ommited from the above formula.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Quenouille, M. H. (1956) Notes on bias in estimation. Biometrika, 43, 353–360.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
Shao, J. and Tu, D. (1995) The Jackknife and Bootstrap. Springer-Verlag, Inc.
Tukey, J. W. (1958) Bias and confidence in not-quite large samples (abstract). The Annals of Mathematical Statistics, 29, 2, p. 614.
Wolter, K. M. (2007) Introduction to Variance Estimation. 2nd Ed. Springer, Inc.
VE.Jk.CBS.HT.Corr.Hajek
VE.Jk.CBS.SYG.Corr.Hajek
VE.Jk.B.Corr.Hajek
VE.Jk.EB.SW2.Corr.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the var. est. of the corr. coeff. point estimator using y1 VE.Jk.Tukey.Corr.Hajek(y1[s==1], x[s==1], pik.U[s==1], N) #Computes the var. est. of the corr. coeff. point estimator using y2 VE.Jk.Tukey.Corr.Hajek(y2[s==1], x[s==1], pik.U[s==1], N, FPC= FALSE)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the var. est. of the corr. coeff. point estimator using y1 VE.Jk.Tukey.Corr.Hajek(y1[s==1], x[s==1], pik.U[s==1], N) #Computes the var. est. of the corr. coeff. point estimator using y2 VE.Jk.Tukey.Corr.Hajek(y2[s==1], x[s==1], pik.U[s==1], N, FPC= FALSE)
Computes the Quenouille(1956); Tukey (1958) jackknife variance estimator for the estimator of a correlation coefficient of two variables using the Narain (1951); Horvitz-Thompson (1952) point estimator.
VE.Jk.Tukey.Corr.NHT(VecY.s, VecX.s, VecPk.s, N, FPC= TRUE)
VE.Jk.Tukey.Corr.NHT(VecY.s, VecX.s, VecPk.s, N, FPC= TRUE)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. This information is also utilised for the finite population correction; see |
FPC |
logical value. If an ad hoc finite population correction |
For the population correlation coefficient of two variables and
:
the point estimator of is given by:
where is the Narain (1951); Horvitz-Thompson (1952) estimator for the population mean
,
and with
denoting the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Quenouille(1956); Tukey (1958) jackknife variance estimator (implemented by the current function):
where has the same functional form as
but omitting the
-th element from the sample
.
We are implementing the Tukey (1958) jackknife variance estimator using the ‘ad hoc’ finite population correction
(see Shao and Tu, 1995; Wolter, 2007). If
FPC=FALSE
, then the term is omitted from the above formula.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Narain, R. D. (1951) On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–175.
Quenouille, M. H. (1956) Notes on bias in estimation. Biometrika, 43, 353–360.
Shao, J. and Tu, D. (1995) The Jackknife and Bootstrap. Springer-Verlag, Inc.
Tukey, J. W. (1958) Bias and confidence in not-quite large samples (abstract). The Annals of Mathematical Statistics, 29, 2, p. 614.
Wolter, K. M. (2007) Introduction to Variance Estimation. 2nd Ed. Springer, Inc.
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the var. est. of the corr. coeff. point estimator using y1 VE.Jk.Tukey.Corr.NHT(y1[s==1], x[s==1], pik.U[s==1], N) #Computes the var. est. of the corr. coeff. point estimator using y2 VE.Jk.Tukey.Corr.NHT(y2[s==1], x[s==1], pik.U[s==1], N, FPC= FALSE)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the var. est. of the corr. coeff. point estimator using y1 VE.Jk.Tukey.Corr.NHT(y1[s==1], x[s==1], pik.U[s==1], N) #Computes the var. est. of the corr. coeff. point estimator using y2 VE.Jk.Tukey.Corr.NHT(y2[s==1], x[s==1], pik.U[s==1], N, FPC= FALSE)
Computes the Quenouille(1956); Tukey (1958) jackknife variance estimator for the Hajek (1971) estimator of a mean.
VE.Jk.Tukey.Mean.Hajek(VecY.s, VecPk.s, N, FPC= TRUE)
VE.Jk.Tukey.Mean.Hajek(VecY.s, VecPk.s, N, FPC= TRUE)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. This information is also utilised for the finite population correction; see |
FPC |
logical value. If an ad hoc finite population correction |
For the population mean of the variable :
the approximately unbiased Hajek (1971) estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Quenouille(1956); Tukey (1958) jackknife variance estimator (implemented by the current function):
where
We are implementing the Tukey (1958) jackknife variance estimator using the ‘ad hoc’ finite population correction (see Shao and Tu, 1995; Wolter, 2007). If
FPC=FALSE
, then the term is omitted from the above formula.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Quenouille, M. H. (1956) Notes on bias in estimation. Biometrika, 43, 353–360.
Shao, J. and Tu, D. (1995) The Jackknife and Bootstrap. Springer-Verlag, Inc.
Tukey, J. W. (1958) Bias and confidence in not-quite large samples (abstract). The Annals of Mathematical Statistics, 29, 2, p. 614.
Wolter, K. M. (2007) Introduction to Variance Estimation. 2nd Ed. Springer, Inc.
VE.Jk.CBS.HT.Mean.Hajek
VE.Jk.CBS.SYG.Mean.Hajek
VE.Jk.B.Mean.Hajek
VE.Jk.EB.SW2.Mean.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #Computes the var. est. of the Hajek mean point estimator using y1 VE.Jk.Tukey.Mean.Hajek(y1[s==1], pik.U[s==1], N) #Computes the var. est. of the Hajek mean point estimator using y2 VE.Jk.Tukey.Mean.Hajek(y2[s==1], pik.U[s==1], N, FPC= FALSE)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #Computes the var. est. of the Hajek mean point estimator using y1 VE.Jk.Tukey.Mean.Hajek(y1[s==1], pik.U[s==1], N) #Computes the var. est. of the Hajek mean point estimator using y2 VE.Jk.Tukey.Mean.Hajek(y2[s==1], pik.U[s==1], N, FPC= FALSE)
Computes the Quenouille(1956); Tukey (1958) jackknife variance estimator for the estimator of a ratio of two totals/means.
VE.Jk.Tukey.Ratio(VecY.s, VecX.s, VecPk.s, N, FPC= TRUE)
VE.Jk.Tukey.Ratio(VecY.s, VecX.s, VecPk.s, N, FPC= TRUE)
VecY.s |
vector of the numerator variable of interest; its length is equal to |
VecX.s |
vector of the denominator variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. This information is also utilised for the finite population correction; see |
FPC |
logical value. If an ad hoc finite population correction |
For the population ratio of two totals/means of the variables and
:
the ratio estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Quenouille(1956); Tukey (1958) jackknife variance estimator (implemented by the current function):
where
We are implementing the Tukey (1958) jackknife variance estimator using the ‘ad hoc’ finite population correction (see Shao and Tu, 1995; Wolter, 2007). If
FPC=FALSE
, then the term is omitted from the above formula.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Quenouille, M. H. (1956) Notes on bias in estimation. Biometrika, 43, 353–360.
Shao, J. and Tu, D. (1995) The Jackknife and Bootstrap. Springer-Verlag, Inc.
Tukey, J. W. (1958) Bias and confidence in not-quite large samples (abstract). The Annals of Mathematical Statistics, 29, 2, p. 614.
Wolter, K. M. (2007) Introduction to Variance Estimation. 2nd Ed. Springer, Inc.
VE.Lin.HT.Ratio
VE.Lin.SYG.Ratio
VE.Jk.CBS.HT.Ratio
VE.Jk.CBS.SYG.Ratio
VE.Jk.B.Ratio
VE.Jk.EB.SW2.Ratio
VE.EB.HT.Ratio
VE.EB.SYG.Ratio
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x #Computes the var. est. of the ratio point estimator using y1 VE.Jk.Tukey.Ratio(y1[s==1], x[s==1], pik.U[s==1], N) #Computes the var. est. of the ratio point estimator using y2 VE.Jk.Tukey.Ratio(y2[s==1], x[s==1], pik.U[s==1], N, FPC= FALSE)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x #Computes the var. est. of the ratio point estimator using y1 VE.Jk.Tukey.Ratio(y1[s==1], x[s==1], pik.U[s==1], N) #Computes the var. est. of the ratio point estimator using y2 VE.Jk.Tukey.Ratio(y2[s==1], x[s==1], pik.U[s==1], N, FPC= FALSE)
Computes the Quenouille(1956); Tukey (1958) jackknife variance estimator for the estimator of the regression coefficient using the Hajek (1971) point estimator.
VE.Jk.Tukey.RegCo.Hajek(VecY.s, VecX.s, VecPk.s, N, FPC= TRUE)
VE.Jk.Tukey.RegCo.Hajek(VecY.s, VecX.s, VecPk.s, N, FPC= TRUE)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. This information is utilised for the finite population correction only; see |
FPC |
logical value. If an ad hoc finite population correction |
From Linear Regression Analysis, for an imposed population model
the population regression coefficient , assuming that the population size
is unknown (see Sarndal et al., 1992, Sec. 5.10), can be estimated by:
where and
are the Hajek (1971) point estimators of the population means
and
, respectively,
and with
denoting the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Quenouille(1956); Tukey (1958) jackknife variance estimator (implemented by the current function):
where has the same functional form as
but omitting the
-th element from the sample
.
We are implementing the Tukey (1958) jackknife variance estimator using the ‘ad hoc’ finite population correction
(see Shao and Tu, 1995; Wolter, 2007). If
FPC=FALSE
, then the term is omitted from the above formula.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Quenouille, M. H. (1956) Notes on bias in estimation. Biometrika, 43, 353–360.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
Shao, J. and Tu, D. (1995) The Jackknife and Bootstrap. Springer-Verlag, Inc.
Tukey, J. W. (1958) Bias and confidence in not-quite large samples (abstract). The Annals of Mathematical Statistics, 29, 2, p. 614.
Wolter, K. M. (2007) Introduction to Variance Estimation. 2nd Ed. Springer, Inc.
VE.Jk.Tukey.RegCoI.Hajek
VE.Jk.CBS.HT.RegCo.Hajek
VE.Jk.CBS.SYG.RegCo.Hajek
VE.Jk.B.RegCo.Hajek
VE.Jk.EB.SW2.RegCo.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the var. est. of the regression coeff. point estimator using y1 VE.Jk.Tukey.RegCo.Hajek(y1[s==1], x[s==1], pik.U[s==1], N) #Computes the var. est. of the regression coeff. point estimator using y2 VE.Jk.Tukey.RegCo.Hajek(y2[s==1], x[s==1], pik.U[s==1], N, FPC= FALSE)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the var. est. of the regression coeff. point estimator using y1 VE.Jk.Tukey.RegCo.Hajek(y1[s==1], x[s==1], pik.U[s==1], N) #Computes the var. est. of the regression coeff. point estimator using y2 VE.Jk.Tukey.RegCo.Hajek(y2[s==1], x[s==1], pik.U[s==1], N, FPC= FALSE)
Computes the Quenouille(1956); Tukey (1958) jackknife variance estimator for the estimator of the intercept regression coefficient using the Hajek (1971) point estimator.
VE.Jk.Tukey.RegCoI.Hajek(VecY.s, VecX.s, VecPk.s, N, FPC= TRUE)
VE.Jk.Tukey.RegCoI.Hajek(VecY.s, VecX.s, VecPk.s, N, FPC= TRUE)
VecY.s |
vector of the variable of interest Y; its length is equal to |
VecX.s |
vector of the variable of interest X; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. This information is utilised for the finite population correction only; see |
FPC |
logical value. If an ad hoc finite population correction |
From Linear Regression Analysis, for an imposed population model
the population intercept regression coefficient , assuming that the population size
is unknown (see Sarndal et al., 1992, Sec. 5.10), can be estimated by:
where and
are the Hajek (1971) point estimators of the population means
and
, respectively,
and with
denoting the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Quenouille(1956); Tukey (1958) jackknife variance estimator (implemented by the current function):
where has the same functional form as
but omitting the
-th element from the sample
.
We are implementing the Tukey (1958) jackknife variance estimator using the ‘ad hoc’ finite population correction
(see Shao and Tu, 1995; Wolter, 2007). If
FPC=FALSE
, then the term is omitted from the above formula.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Quenouille, M. H. (1956) Notes on bias in estimation. Biometrika, 43, 353–360.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
Shao, J. and Tu, D. (1995) The Jackknife and Bootstrap. Springer-Verlag, Inc.
Tukey, J. W. (1958) Bias and confidence in not-quite large samples (abstract). The Annals of Mathematical Statistics, 29, 2, p. 614.
Wolter, K. M. (2007) Introduction to Variance Estimation. 2nd Ed. Springer, Inc.
VE.Jk.Tukey.RegCo.Hajek
VE.Jk.CBS.HT.RegCoI.Hajek
VE.Jk.CBS.SYG.RegCoI.Hajek
VE.Jk.B.RegCoI.Hajek
VE.Jk.EB.SW2.RegCoI.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the var. est. of the intercept reg. coeff. point estimator using y1 VE.Jk.Tukey.RegCoI.Hajek(y1[s==1], x[s==1], pik.U[s==1], N) #Computes the var. est. of the intercept reg. coeff. point estimator using y2 VE.Jk.Tukey.RegCoI.Hajek(y2[s==1], x[s==1], pik.U[s==1], N, FPC= FALSE)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 x <- oaxaca$HOMES10 #Defines the variable of interest x #Computes the var. est. of the intercept reg. coeff. point estimator using y1 VE.Jk.Tukey.RegCoI.Hajek(y1[s==1], x[s==1], pik.U[s==1], N) #Computes the var. est. of the intercept reg. coeff. point estimator using y2 VE.Jk.Tukey.RegCoI.Hajek(y2[s==1], x[s==1], pik.U[s==1], N, FPC= FALSE)
Computes the Quenouille(1956); Tukey (1958) jackknife variance estimator for the Hajek (1971) estimator of a total.
VE.Jk.Tukey.Total.Hajek(VecY.s, VecPk.s, N, FPC= TRUE)
VE.Jk.Tukey.Total.Hajek(VecY.s, VecPk.s, N, FPC= TRUE)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. This information is also utilised for the finite population correction; see |
FPC |
logical value. If an ad hoc finite population correction |
For the population total of the variable :
the approximately unbiased Hajek (1971) estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the Quenouille(1956); Tukey (1958) jackknife variance estimator (implemented by the current function):
where
We are implementing the Tukey (1958) jackknife variance estimator using the ‘ad hoc’ finite population correction (see Shao and Tu, 1995; Wolter, 2007). If
FPC=FALSE
, then the term is omitted from the above formula.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Hajek, J. (1971) Comment on An essay on the logical foundations of survey sampling by Basu, D. in Foundations of Statistical Inference (Godambe, V.P. and Sprott, D.A. eds.), p. 236. Holt, Rinehart and Winston.
Quenouille, M. H. (1956) Notes on bias in estimation. Biometrika, 43, 353–360.
Shao, J. and Tu, D. (1995) The Jackknife and Bootstrap. Springer-Verlag, Inc.
Tukey, J. W. (1958) Bias and confidence in not-quite large samples (abstract). The Annals of Mathematical Statistics, 29, 2, p. 614.
Wolter, K. M. (2007) Introduction to Variance Estimation. 2nd Ed. Springer, Inc.
VE.Jk.CBS.HT.Total.Hajek
VE.Jk.CBS.SYG.Total.Hajek
VE.Jk.B.Total.Hajek
VE.Jk.EB.SW2.Total.Hajek
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #Computes the var. est. of the Hajek total point estimator using y1 VE.Jk.Tukey.Total.Hajek(y1[s==1], pik.U[s==1], N) #Computes the var. est. of the Hajek total point estimator using y2 VE.Jk.Tukey.Total.Hajek(y2[s==1], pik.U[s==1], N, FPC= FALSE)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$POPMAL10 #Defines the variable of interest y2 #Computes the var. est. of the Hajek total point estimator using y1 VE.Jk.Tukey.Total.Hajek(y1[s==1], pik.U[s==1], N) #Computes the var. est. of the Hajek total point estimator using y2 VE.Jk.Tukey.Total.Hajek(y2[s==1], pik.U[s==1], N, FPC= FALSE)
Computes the unequal probability Taylor linearisation variance estimator for the estimator of a ratio of two totals/means. It uses the Horvitz-Thompson (1952) variance form.
VE.Lin.HT.Ratio(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VE.Lin.HT.Ratio(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VecY.s |
vector of the numerator variable of interest; its length is equal to |
VecX.s |
vector of the denominator variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
For the population ratio of two totals/means of the variables and
:
the ratio estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the unequal probability linearisation variance estimator (implemented by the current function). For details see Woodruff (1971); Deville (1999); Demnati-Rao (2004); Sarndal et al., (1992, Secs. 5.5 and 5.6):
where
with
the unbiased Narain (1951); Horvitz-Thompson (1952) estimator of the population total for the (denominator) variable VecX.s
.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Demnati, A. and Rao, J. N. K. (2004) Linearization variance estimators for survey data. Survey Methodology, 30, 17–26.
Deville, J.-C. (1999) Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193–203.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Narain, R. D. (1951) On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–175.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
Woodruff, R. S. (1971) A Simple Method for Approximating the Variance of a Complicated Estimate. Journal of the American Statistical Association, 66, 334, 411–414.
VE.Lin.SYG.Ratio
VE.Jk.Tukey.Ratio
VE.Jk.CBS.SYG.Ratio
VE.Jk.B.Ratio
VE.Jk.EB.SW2.Ratio
VE.EB.HT.Ratio
VE.EB.SYG.Ratio
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the ratio point estimator using y1 VE.Lin.HT.Ratio(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the ratio point estimator using y2 VE.Lin.HT.Ratio(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the ratio point estimator using y1 VE.Lin.HT.Ratio(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the ratio point estimator using y2 VE.Lin.HT.Ratio(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
Computes the unequal probability Taylor linearisation variance estimator for the estimator of a ratio of two totals/means. It uses the Sen (1953); Yates-Grundy(1953) variance form.
VE.Lin.SYG.Ratio(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VE.Lin.SYG.Ratio(VecY.s, VecX.s, VecPk.s, MatPkl.s)
VecY.s |
vector of the numerator variable of interest; its length is equal to |
VecX.s |
vector of the denominator variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
For the population ratio of two totals/means of the variables and
:
the ratio estimator of is given by:
where and
denotes the inclusion probability of the
-th element in the sample
. The variance of
can be estimated by the unequal probability linearisation variance estimator (implemented by the current function). For details see Woodruff (1971); Deville (1999); Demnati-Rao (2004); Sarndal et al., (1992, Secs. 5.5 and 5.6):
where
with
the unbiased Narain (1951); Horvitz-Thompson (1952) estimator of the population total for the (denominator) variable VecX.s
.
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Demnati, A. and Rao, J. N. K. (2004) Linearization variance estimators for survey data. Survey Methodology, 30, 17–26.
Deville, J.-C. (1999) Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193–203.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Narain, R. D. (1951) On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–175.
Sarndal, C.-E. and Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer-Verlag, Inc.
Sen, A. R. (1953) On the estimate of the variance in sampling with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 5, 119–127.
Woodruff, R. S. (1971) A Simple Method for Approximating the Variance of a Complicated Estimate. Journal of the American Statistical Association, 66, 334, 411–414.
Yates, F. and Grundy, P. M. (1953) Selection without replacement from within strata with probability proportional to size. Journal of the Royal Statistical Society B, 15, 253–261.
VE.Lin.HT.Ratio
VE.Jk.Tukey.Ratio
VE.Jk.CBS.HT.Ratio
VE.Jk.B.Ratio
VE.Jk.EB.SW2.Ratio
VE.EB.HT.Ratio
VE.EB.SYG.Ratio
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used for y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the ratio point estimator using y1 VE.Lin.SYG.Ratio(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the ratio point estimator using y2 VE.Lin.SYG.Ratio(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used for y1 <- oaxaca$POP10 #Defines the numerator variable y1 y2 <- oaxaca$POPMAL10 #Defines the numerator variable y2 x <- oaxaca$HOMES10 #Defines the denominator variable x #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the ratio point estimator using y1 VE.Lin.SYG.Ratio(y1[s==1], x[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the ratio point estimator using y2 VE.Lin.SYG.Ratio(y2[s==1], x[s==1], pik.U[s==1], pikl.s)
Computes the Sen (1953); Yates-Grundy(1953) variance estimator for the Narain (1951); Horvitz-Thompson (1952) point estimator for a population mean.
VE.SYG.Mean.NHT(VecY.s, VecPk.s, MatPkl.s, N)
VE.SYG.Mean.NHT(VecY.s, VecPk.s, MatPkl.s, N)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
N |
the population size. It must be an integer or a double-precision scalar with zero-valued fractional part. |
For the population mean of the variable :
the unbiased Narain (1951); Horvitz-Thompson (1952) estimator of is given by:
where denotes the inclusion probability of the
-th element in the sample
. Let
denotes the joint-inclusion probabilities of the
-th and
-th elements in the sample
. The variance of
is given by:
which, if the utilised sampling design is of fixed sample size, can therefore be estimated by the Sen-Yates-Grundy variance estimator (implemented by the current function):
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Narain, R. D. (1951) On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–175.
Sen, A. R. (1953) On the estimate of the variance in sampling with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 5, 119–127.
Yates, F. and Grundy, P. M. (1953) Selection without replacement from within strata with probability proportional to size. Journal of the Royal Statistical Society B, 15, 253–261.
VE.HT.Mean.NHT
VE.Hajek.Mean.NHT
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 #This approx. is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the NHT point estimator for y1 VE.SYG.Mean.NHT(y1[s==1], pik.U[s==1], pikl.s, N) #Computes the var. est. of the NHT point estimator for y2 VE.SYG.Mean.NHT(y2[s==1], pik.U[s==1], pikl.s, N)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used N <- dim(oaxaca)[1] #Defines the population size y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 #This approx. is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the NHT point estimator for y1 VE.SYG.Mean.NHT(y1[s==1], pik.U[s==1], pikl.s, N) #Computes the var. est. of the NHT point estimator for y2 VE.SYG.Mean.NHT(y2[s==1], pik.U[s==1], pikl.s, N)
Computes the Sen (1953); Yates-Grundy(1953) variance estimator for the Narain (1951); Horvitz-Thompson (1952) point estimator for a population total.
VE.SYG.Total.NHT(VecY.s, VecPk.s, MatPkl.s)
VE.SYG.Total.NHT(VecY.s, VecPk.s, MatPkl.s)
VecY.s |
vector of the variable of interest; its length is equal to |
VecPk.s |
vector of the first-order inclusion probabilities; its length is equal to |
MatPkl.s |
matrix of the second-order inclusion probabilities; its number of rows and columns equals |
For the population total of the variable :
the unbiased Narain (1951); Horvitz-Thompson (1952) estimator of is given by:
where denotes the inclusion probability of the
-th element in the sample
. Let
denotes the joint-inclusion probabilities of the
-th and
-th elements in the sample
. The variance of
is given by:
which, if the utilised sampling design is of fixed sample size, can therefore be estimated by the Sen-Yates-Grundy variance estimator (implemented by the current function):
The function returns a value for the estimated variance.
Emilio Lopez Escobar.
Horvitz, D. G. and Thompson, D. J. (1952) A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
Narain, R. D. (1951) On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–175.
Sen, A. R. (1953) On the estimate of the variance in sampling with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 5, 119–127.
Yates, F. and Grundy, P. M. (1953) Selection without replacement from within strata with probability proportional to size. Journal of the Royal Statistical Society B, 15, 253–261.
VE.HT.Total.NHT
VE.Hajek.Total.NHT
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the NHT point estimator for y1 VE.SYG.Total.NHT(y1[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the NHT point estimator for y2 VE.SYG.Total.NHT(y2[s==1], pik.U[s==1], pikl.s)
data(oaxaca) #Loads the Oaxaca municipalities dataset pik.U <- Pk.PropNorm.U(373, oaxaca$HOMES00) #Reconstructs the 1st order incl. probs. s <- oaxaca$sHOMES00 #Defines the sample to be used y1 <- oaxaca$POP10 #Defines the variable of interest y1 y2 <- oaxaca$HOMES10 #Defines the variable of interest y2 #This approximation is only suitable for large-entropy sampling designs pikl.s <- Pkl.Hajek.s(pik.U[s==1]) #Approx. 2nd order incl. probs. from s #Computes the var. est. of the NHT point estimator for y1 VE.SYG.Total.NHT(y1[s==1], pik.U[s==1], pikl.s) #Computes the var. est. of the NHT point estimator for y2 VE.SYG.Total.NHT(y2[s==1], pik.U[s==1], pikl.s)