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QuantRegGLasso
View on CRAN: Click
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Download and install QuantRegGLasso package within the R console
Install from CRAN:
install.packages("QuantRegGLasso")
Install from Github:
library("remotes")
install_github("cran/QuantRegGLasso")
Install by package version:
library("remotes")
install_version("QuantRegGLasso", "1.0.0")
Attach the package and use:
library("QuantRegGLasso")
Maintained by
Wen-Ting Wang
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
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First Published: 2024-01-16
Latest Update: 2024-01-16
Description:
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, ).
How to cite:
Wen-Ting Wang (2024). QuantRegGLasso: Adaptively Weighted Group Lasso for Semiparametric Quantile Regression Models. R package version 1.0.0, https://cran.r-project.org/web/packages/QuantRegGLasso. Accessed 07 Nov. 2024.
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