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Implements an adaptively weighted group Lasso procedure for simultaneous variable selection and structure identification in varying coefficient quantile regression models and additive quantile regression models with ultra-high dimensional covariates. The methodology, grounded in a strong sparsity condition, establishes selection consistency under certain weight conditions. To address the challenge of tuning parameter selection in practice, a BIC-type criterion named high-dimensional information criterion (HDIC) is proposed. The Lasso procedure, guided by HDIC-determined tuning parameters, maintains selection consistency. Theoretical findings are strongly supported by simulation studies. (Toshio Honda, Ching-Kang Ing, Wei-Ying Wu, 2019, <doi:10.3150/18-BEJ1091>).
Version: | 1.0.0 |
Depends: | R (≥ 3.4.0) |
Imports: | Rcpp (≥ 1.0.12), ggplot2 |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | knitr, rmarkdown, testthat (≥ 2.1.0) |
Published: | 2024-01-16 |
DOI: | 10.32614/CRAN.package.QuantRegGLasso |
Author: | Wen-Ting Wang |
Maintainer: | Wen-Ting Wang <egpivo at gmail.com> |
BugReports: | https://github.com/egpivo/QuantRegGLasso/issues |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
URL: | https://github.com/egpivo/SpatPCA |
NeedsCompilation: | yes |
SystemRequirements: | GNU make |
Materials: | README NEWS |
CRAN checks: | QuantRegGLasso results |
Reference manual: | QuantRegGLasso.pdf |
Package source: | QuantRegGLasso_1.0.0.tar.gz |
Windows binaries: | r-devel: QuantRegGLasso_1.0.0.zip, r-release: QuantRegGLasso_1.0.0.zip, r-oldrel: QuantRegGLasso_1.0.0.zip |
macOS binaries: | r-release (arm64): QuantRegGLasso_1.0.0.tgz, r-oldrel (arm64): QuantRegGLasso_1.0.0.tgz, r-release (x86_64): QuantRegGLasso_1.0.0.tgz, r-oldrel (x86_64): QuantRegGLasso_1.0.0.tgz |
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