Composite quantile regression and variable selection in single-index coefficient model

DOI10.1016/j.jspi.2016.04.003zbMath1338.62100OpenAlexW2344647533MaRDI QIDQ286468

Yazhao Lv, Ri-quan Zhang, Ji-cai Liu, Weihua Zhao

Publication date: 20 May 2016

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2016.04.003

zbMATH Keywords

asymptotic normalityasymptotic relative efficiencyvariable selectioncomposite quantile regressionadaptive LASSOsingle index coefficient model

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Nonparametric estimation (62G05)

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Composite quantile regression for heteroscedastic partially linear varying-coefficient models with missing censoring indicators ⋮ High-dimensional Varying Index Coefficient Quantile Regression Model ⋮ B-spline estimation for partially linear varying coefficient composite quantile regression models ⋮ Composite quantile regression analysis of survival data with missing cause-of-failure information and its application to breast cancer clinical trial ⋮ Weighted composite quantile regression for partially linear varying coefficient models ⋮ Penalized weighted composite quantile regression for partially linear varying coefficient models with missing covariates ⋮ Computation and application of robust data-driven bandwidth selection for gradient function estimation

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