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THE LASSO METHOD FOR VARIABLE SELECTION IN THE COX MODEL
Author(s) -
TIBSHIRANI ROBERT
Publication year - 1997
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19970228)16:4<385::aid-sim380>3.0.co;2-3
Subject(s) - lasso (programming language) , constraint (computer aided design) , context (archaeology) , mathematics , model selection , feature selection , variance (accounting) , selection (genetic algorithm) , statistics , proportional hazards model , bounded function , computer science , artificial intelligence , paleontology , mathematical analysis , geometry , accounting , world wide web , business , biology
I propose a new method for variable selection and shrinkage in Cox's proportional hazards model. My proposal minimizes the log partial likelihood subject to the sum of the absolute values of the parameters being bounded by a constant. Because of the nature of this constraint, it shrinks coefficients and produces some coefficients that are exactly zero. As a result it reduces the estimation variance while providing an interpretable final model. The method is a variation of the ‘lasso’ proposal of Tibshirani, designed for the linear regression context. Simulations indicate that the lasso can be more accurate than stepwise selection in this setting. © 1997 by John Wiley & Sons, Ltd.