Best subsets variable selection in nonnormal regression models

Charles Lindsey, Simon Sheather

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

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 nonnormal regression models. Our method is described in Lawless and Singhal (1978, Biometrics 34: 318-327).

Original languageEnglish
Article numberst0413
Pages (from-to)1046-1059
Number of pages14
JournalStata Journal
Volume15
Issue number4
DOIs
StatePublished - Dec 2015

Bibliographical note

Publisher Copyright:
© 2015 StataCorp LP.

Keywords

  • Gvselect
  • Regress
  • St0413
  • Variable selection
  • Vselect

ASJC Scopus subject areas

  • Mathematics (miscellaneous)

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