Variable Selection in Semiparametric Linear Regression with Censored Data

Abstract

Summary We describe two procedures for selecting variables in the semiparametric linear regression model for censored data. One procedure penalizes a vector of estimating equations and simultaneously estimates regression coefficients and selects submodels. A second procedure controls systematically the proportion of unimportant variables through forward selection and the addition of pseudorandom variables. We explore both rank-based statistics and Buckley–James statistics in the setting proposed and evaluate the performance of all methods through extensive simulation studies and one real data set.