Variable selection in high-dimensional quantile varying coefficient models

Yanlin Tang, Xinyuan Song, Huixia Judy Wang and Zhongyi Zhu

Journal of Multivariate Analysis, 2013, vol. 122, issue C, 115-132

Abstract: In this paper, we propose a two-stage variable selection procedure for high dimensional quantile varying coefficient models. The proposed method is based on basis function approximation and LASSO-type penalties. We show that the first stage penalized estimator with LASSO penalty reduces the model from ultra-high dimensional to a model that has size close to the true model, but contains the true model as a valid sub model. By applying adaptive LASSO penalty to the reduced model, the second stage excludes the remained irrelevant covariates, leading to an estimator consistent in variable selection. A simulation study and the analysis of a real data demonstrate that the proposed method performs quite well in finite samples, with regard to dimension reduction and variable selection.

Keywords: B-spline; High dimensional; LASSO; Linear programming; Nonparametric (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (23)

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DOI: 10.1016/j.jmva.2013.07.015

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