Parametric variable selection in generalized partially linear models with an application to assess condom use by HIV-infected patients

Chenlei Leng, Hua Liang, Neil Martinson

Research output: Contribution to journalArticlepeer-review

Abstract

To study significant predictors of condom use in HIV-infected adults, we propose the use of generalized partially linear models and develop a variable selection procedure incorporating a least squares approximation. Local polynomial regression and spline smoothing techniques are used to estimate the baseline nonparametric function. The asymptotic normality of the resulting estimate is established. We further demonstrate that, with the proper choice of the penalty functions and the regularization parameter, the resulting estimate performs as well as an oracle procedure. Finite sample performance of the proposed inference procedure is assessed by Monte Carlo simulation studies. An application to assess condom use by HIV-infected patients gains some interesting results, which cannot be obtained when an ordinary logistic model is used.

Original languageEnglish (US)
Pages (from-to)2015-2027
Number of pages13
JournalStatistics in Medicine
Volume30
Issue number16
DOIs
StatePublished - Jul 20 2011

Keywords

  • AIDS
  • Condom use
  • LASSO
  • Least squares approximation
  • Local linear regression
  • Profile likeli-hood
  • Quasilikelihood
  • SCAD
  • Sexual behavior
  • Spline smoothing

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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