Variable selection in semiparametric linear regression with censored data

Brent A. Johnson

Journal of the Royal Statistical Society Series B, 2008, vol. 70, issue 2, 351-370

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.

Date: 2008
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