title: Parametric variable selection in generalized partially linear models with an application to assess condom use by HIV-infected patients.
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.
authors.affiliation: Department of Statistics and Applied Probability, National University of Singapore, Singapore. stalc@nus.edu.sg
authors.forename: Chenlei
authors.initials: C
authors.lastname: Leng
authors.forename: Hua
authors.initials: H
authors.lastname: Liang
authors.forename: Neil
authors.initials: N
authors.lastname: Martinson
mesh_terms: D000328:Adult; D056808:Biostatistics / Q000379:methods*; D017280:Condoms / Q000706:statistics & numerical data*; D005260:Female; D015658:HIV Infections / Q000517:prevention & control* / Q000635:transmission*; D006801:Humans; D016018:Least-Squares Analysis; D016013:Likelihood Functions; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D009010:Monte Carlo Method; D012725:Sexual Behavior; D013019:South Africa