Basic Info | Problem setup | Methodology | 1. Estimation: sparse EM algorithm | 2. Statistical inference | 2.a Gaussian test statistic matrix | 2.b Global testing | 2.c Simultaneous testing | Quick start | Reference
Package: hdiVAR 1.0.2
Xiang Lyu
hdiVAR: Statistical Inference for Noisy Vector Autoregression
The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space model. Provable sparse expectation-maximization algorithm is provided for the estimation of transition matrix and noise variances. Global and simultaneous testings are implemented for transition matrix with false discovery rate control. For more information, see the accompanying paper: Lyu, X., Kang, J., & Li, L. (2023). "Statistical inference for high-dimensional vector autoregression with measurement error", Statistica Sinica.
Authors:
hdiVAR_1.0.2.tar.gz
hdiVAR_1.0.2.tar.gz(r-4.7-any)hdiVAR_1.0.2.tar.gz(r-4.6-any)
hdiVAR_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
hdiVAR/json (API)
| # Install 'hdiVAR' in R: |
| install.packages('hdiVAR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:b10b8997ca. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 106 | ||
| source / vignettes | OK | 162 | ||
| linux-release-x86_64 | OK | 110 | ||
| wasm-release | OK | 98 |
