Variable Selection for Semiparametric Partially Linear Covariate-Adjusted Regression Models

Jiang Du, Gaorong Li, Heng PENG*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)


In this article, the partially linear covariate-adjusted regression models are considered, and the penalized least-squares procedure is proposed to simultaneously select variables and estimate the parametric components. The rate of convergence and the asymptotic normality of the resulting estimators are established under some regularization conditions. With the proper choices of the penalty functions and tuning parameters, it is shown that the proposed procedure can be as efficient as the oracle estimators. Some Monte Carlo simulation studies and a real data application are carried out to assess the finite sample performances for the proposed method.

Original languageEnglish
Pages (from-to)2809-2826
Number of pages18
JournalCommunications in Statistics - Theory and Methods
Issue number13
Publication statusPublished - 3 Jul 2015

Scopus Subject Areas

  • Statistics and Probability

User-Defined Keywords

  • Covariate-adjusted regression
  • Oracle property
  • Partially linear model
  • Variable selection


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