# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "QuantRegGLasso" in publications use:' type: software license: GPL-2.0-or-later title: 'QuantRegGLasso: Adaptively Weighted Group Lasso for Semiparametric Quantile Regression Models' version: 1.0.0 doi: 10.32614/CRAN.package.QuantRegGLasso abstract: 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, ). authors: - family-names: Wang given-names: Wen-Ting email: egpivo@gmail.com orcid: https://orcid.org/0000-0003-3051-7302 - family-names: Wu given-names: Wei-Ying email: wuweiying1011@gmail.com - family-names: Honda given-names: Toshio email: t.honda@r.hit-u.ac.jp - family-names: Ing given-names: Ching-Kang email: cking@stat.nthu.edu.tw orcid: https://orcid.org/0000-0003-1362-8246 repository: https://CRAN.R-project.org/package=QuantRegGLasso repository-code: https://github.com/egpivo/QuantRegGLasso url: https://github.com/egpivo/SpatPCA date-released: '2024-01-16' contact: - family-names: Wang given-names: Wen-Ting email: egpivo@gmail.com orcid: https://orcid.org/0000-0003-3051-7302