Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Two-Stage Penalized Composite Quantile Regression with Grouped Variables

  • Received : 2013.02.19
  • Accepted : 2013.06.12
  • Published : 2013.07.31
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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.

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References

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