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, pages 351-370

Abstract: 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. Copyright (c) 2008 The Authors.

Date: 2008

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