| Title: | Minimum Distance Estimation in Linear Regression Model |
|---|---|
| Description: | Consider linear regression model Y = Xb + error where the distribution function of errors is unknown, but errors are independent and symmetrically distributed. The package contains a function named LRMDE which takes Y and X as input and returns minimum distance estimator of parameter b in the model. |
| Authors: | Jiwoong Kim [aut, cre] |
| Maintainer: | Jiwoong Kim <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0 |
| Built: | 2024-12-01 08:45:41 UTC |
| Source: | CRAN |
Performs minimum distance estimation in linear regression model: Y=Xb + error
LRMDE(Y, X)LRMDE(Y, X)
Y |
- Response variable in linear regression model |
X |
- Explanatory variable in linear regression model |
Returns betahat - Minimum distance estimator of b
[1] Koul, H. L (1985). Minimum distance estimation in linear regression with unknown error distributions. Statist. Probab. Lett., 3 1-8.
[2] Koul, H. L (1986). Minimum distance estimation and goodness-of-fit tests in first-order autoregression. Ann. Statist., 14 1194-1213.
[3] Koul, H. L (2002). Weighted empirical process in nonlinear dynamic models. Springer, Berlin, Vol. 166
ARMDE
X <- matrix(c(1,1,3,4, 4,2), nrow=3, ncol=2) Y <- c(1,-5, 8) bhat <- LRMDE(Y,X)X <- matrix(c(1,1,3,4, 4,2), nrow=3, ncol=2) Y <- c(1,-5, 8) bhat <- LRMDE(Y,X)