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Variable selection in semiparametric linear regression with censored data

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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.

Keywords: False selection rate; Hard thresholding; Non-smooth estimating function; Rank regression; Soft thresholding; Survival analysis

Document Type: Research Article


Affiliations: Emory University, Atlanta, USA

Publication date: April 1, 2008

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