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Variable selection in semiparametric linear regression with censored data
Author(s) -
Johnson Brent A.
Publication year - 2008
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2008.00639.x
Subject(s) - semiparametric regression , statistics , linear regression , data set , feature selection , econometrics , mathematics , regression analysis , censored regression model , regression , rank (graph theory) , computer science , variables , artificial intelligence , combinatorics
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.