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Fast FSR Variable Selection with Applications to Clinical Trials
Author(s) -
Boos Dennis D.,
Stefanski Leonard A.,
Wu Yujun
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01127.x
Subject(s) - selection (genetic algorithm) , variable (mathematics) , computer science , artificial intelligence , mathematics , mathematical analysis
Summary A new version of the false selection rate variable selection method of Wu, Boos, and Stefanski (2007, Journal of the American Statistical Association 102 , 235–243) is developed that requires no simulation. This version allows the tuning parameter in forward selection to be estimated simply by hand calculation from a summary table of output even for situations where the number of explanatory variables is larger than the sample size. Because of the computational simplicity, the method can be used in permutation tests and inside bagging loops for improved prediction. Illustration is provided in clinical trials for linear regression, logistic regression, and Cox proportional hazards regression.