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Variable selection for additive partially linear models with measurement error

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Abstract

Variable selection for additive partially linear models with measurement error is considered. By the backfitting technique, we first propose a variable selection procedure for the parametric components based on the smoothly clipped absolute deviation (SCAD) penalization, and one-step spare estimates for parametric components are also presented. The resulting estimates perform asymptotic normality as well as an oracle property. Then, two-stage backfitting estimators are also presented for the nonparametric components by using the local linear method, and the structures of asymptotic biases and covariances of the proposed estimators are the same as those in partially linear model with measurement error. The finite sample performance of the proposed procedures is illustrated by simulation studies.

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Authors and Affiliations

  1. Department of Mathematics, Tongji University, 200092, Shanghai, People’s Republic of China

    Zhangong Zhou, Rong Jiang & Weimin Qian

  2. Department of Statistics, Jiaxing University, 314001, Zhejiang, People’s Republic of China

    Zhangong Zhou

Authors
  1. Zhangong Zhou
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  2. Rong Jiang
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  3. Weimin Qian
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Correspondence to Zhangong Zhou.

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Zhou, Z., Jiang, R. & Qian, W. Variable selection for additive partially linear models with measurement error. Metrika 74, 185–202 (2011). https://doi.org/10.1007/s00184-009-0296-6

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  • DOI: https://doi.org/10.1007/s00184-009-0296-6

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