Variable Selection for Partially Linear Models with Randomly Censored Data |
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Authors: | Yiping Yang Liugen Xue Weihu Cheng |
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Affiliation: | 1. College of Mathematics and Statistics , Chongqing Technology and Business University , Chongqing , China yeepingyang@gmail.com;3. College of Applied Sciences , Beijing University of Technology , Beijing , China |
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Abstract: | This article proposes a variable selection procedure for partially linear models with right-censored data via penalized least squares. We apply the SCAD penalty to select significant variables and estimate unknown parameters simultaneously. The sampling properties for the proposed procedure are investigated. The rate of convergence and the asymptotic normality of the proposed estimators are established. Furthermore, the SCAD-penalized estimators of the nonzero coefficients are shown to have the asymptotic oracle property. In addition, an iterative algorithm is proposed to find the solution of the penalized least squares. Simulation studies are conducted to examine the finite sample performance of the proposed method. |
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Keywords: | Censored data Oracle property Partially linear models SCAD Variable selection |
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