Variable Selection in Semiparametric Quantile Modeling for Longitudinal Data

Kangning Wang and Lu Lin

Communications in Statistics - Theory and Methods, 2015, vol. 44, issue 11, 2243-2266

Abstract: We propose a penalized quantile regression for partially linear varying coefficient (VC) model with longitudinal data to select relevant non parametric and parametric components simultaneously. Selection consistency and oracle property are established. Furthermore, if linear part and VC part are unknown, we propose a new unified method, which can do three types of selections: separation of varying and constant effects, selection of relevant variables, and it can be carried out conveniently in one step. Consistency in the three types of selections and oracle property in estimation are established as well. Simulation studies and real data analysis also confirm our method.

Date: 2015
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DOI: 10.1080/03610926.2013.857418

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