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Variable selection for semiparametric varying-coefficient partially linear models with missing response at random

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Abstract

In this paper, we present a variable selection procedure by combining basis function approximations with penalized estimating equations for semiparametric varying-coefficient partially linear models with missing response at random. The proposed procedure simultaneously selects significant variables in parametric components and nonparametric components. With appropriate selection of the tuning parameters, we establish the consistency of the variable selection procedure and the convergence rate of the regularized estimators. A simulation study is undertaken to assess the finite sample performance of the proposed variable selection procedure.

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Authors and Affiliations

  1. Department of Mathematics, Hechi University, Yizhou, 546300, P. R. China

    Pei Xin Zhao

  2. College of Applied Sciences, Beijing University of Technology, Beijing, 100124, P. R. China

    Liu Gen Xue

Authors
  1. Pei Xin Zhao
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  2. Liu Gen Xue
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Correspondence to Pei Xin Zhao.

Additional information

Supported by National Natural Science Foundation of China (Grant No. 10871013), Natural Science Foundation of Beijing (Grant No. 1072004), and Natural Science Foundation of Guangxi Province (Grant No. 2010GXNSFB013051)

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Zhao, P.X., Xue, L.G. Variable selection for semiparametric varying-coefficient partially linear models with missing response at random. Acta. Math. Sin.-English Ser. 27, 2205–2216 (2011). https://doi.org/10.1007/s10114-011-9200-1

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  • DOI: https://doi.org/10.1007/s10114-011-9200-1

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