Two-Stage Penalized Composite Quantile Regression with Grouped Variables
분야
자연과학 > 통계학
저자
( Sung Wan Bang ) , ( Myoung Shic Jhun )
발행기관
한국통계학회
간행물정보
CSAM(Communications for Statistical Applications and Methods) 2013년, 제20권 제4호, 259~270페이지(총12페이지)
파일형식
02706316.pdf [무료 PDF 뷰어 다운로드]
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    영문초록
    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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