Variable selection for partially linear models with measurement errors

Hua Liang, Runze Li

Research output: Contribution to journalArticlepeer-review

111 Scopus citations

Abstract

This article focuses on variable selection for partially linear models when the covariates are measured with additive errors.We propose two classes of variable selection procedures, penalized least squares and penalized quantile regression, using the nonconvex penalized principle. The first procedure corrects the bias in the loss function caused by the measurement error by applying the so-called correction-forattenuation approach, whereas the second procedure corrects the bias by using orthogonal regression. The sampling properties for the two procedures are investigated. The rate of convergence and the asymptotic normality of the resulting estimates are established. We further demonstrate that, with proper choices of the penalty functions and the regularization parameter, the resulting estimates perform asymptotically as well as an oracle property. Choice of smoothing parameters is also discussed. Finite sample performance of the proposed variable selection procedures is assessed by Monte Carlo simulation studies. We further illustrate the proposed procedures by an application.

Original languageEnglish (US)
Pages (from-to)234-248
Number of pages15
JournalJournal of the American Statistical Association
Volume104
Issue number485
DOIs
StatePublished - Mar 2009

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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