QuantRegGLasso: Adaptively Weighted Group Lasso for Semiparametric Quantile
Regression Models
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>).
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[aut, cre],
Wei-Ying Wu [aut],
Toshio Honda [aut],
Ching-Kang Ing