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http://dx.doi.org/10.5351/CSAM.2013.20.4.259

Two-Stage Penalized Composite Quantile Regression with Grouped Variables

Bang, Sungwan (Department of Mathematics, Korea Military Academy)
Jhun, Myoungshic (Department of Statistics, Korea University)

Publication Information

Communications for Statistical Applications and Methods / v.20, no.4, 2013 , pp. 259-270 More about this Journal

Abstract

This paper considers a penalized composite quantile regression (CQR) that performs a variable selection in the linear model with grouped variables. An adaptive sup-norm penalized CQR (ASCQR) is proposed to select variables in a grouped manner; in addition, the consistency and oracle property of the resulting estimator are also derived under some regularity conditions. To improve the efficiency of estimation and variable selection, this paper suggests the two-stage penalized CQR (TSCQR), which uses the ASCQR to select relevant groups in the first stage and the adaptive lasso penalized CQR to select important variables in the second stage. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.

Keywords

Composite quantile regression; factor selection; penalization; sup-norm; variable selection;

Citations & Related Records

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