Best subsets variable selection in nonnormal regression models

Charles Lindsey () and Simon Sheather
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Charles Lindsey: StataCorp
Simon Sheather: Texas A&M Statistics

Stata Journal, 2015, vol. 15, issue 4, 1046-1059

Abstract: We present a new program, gvselect, that helps users perform variable selection in regression. Best subsets variable selection is performed and provides the user with the best combinations of predictors for each level of model complexity. The leaps-and-bounds (Furnival and Wilson, 1974, Technometrics 16: 499–511) algorithm is applied using the log likelihoods of candidate models. This allows the user to perform variable selection on a wide variety of normal and non-normal regression models. Our method is described in Lawless and Singhal (1978, Biometrics 34: 318–327). Copyright 2015 by StataCorp LP.

Keywords: gvselect; regress; vselect; variable selection (search for similar items in EconPapers)
Date: 2015
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