QuantRegGLasso
type: Item
instance of: R package
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, <doi:10.3150/18-BEJ1091>).
last update: 2024-01-16T00:00:00Z
software version identifier: 1.0.0
author: Wen-Ting Wang
author: Wei-Ying Wu
author: Toshio Honda
author: Ching-Kang Ing
maintained by: Wen-Ting Wang
copyright license: GNU General Public License, version 2.0
copyright license: GNU General Public License, version 3.0
imports: Rcpp
imports: ggplot2
cites work: Adaptively weighted group Lasso for semiparametric quantile regression models
CRAN project: QuantRegGLasso
programmed in: R
depends on software: R
MaRDI profile type: Software
description: Adaptively Weighted Group Lasso for Semiparametric Quantile Regression Models