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Best subsets variable selection in nonnormal regression models

Author

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  • Charles Lindsey

    (StataCorp)

  • Simon Sheather

    (Texas A&M Statistics)

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.

Suggested Citation

  • Charles Lindsey & Simon Sheather, 2015. "Best subsets variable selection in nonnormal regression models," Stata Journal, StataCorp LP, vol. 15(4), pages 1046-1059, December.
    • Handle: RePEc:tsj:stataj:v:15:y:2015:i:4:p:1046-1059
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    1. Giuseppe Migliara & Erika Renzi & Valentina Baccolini & Ambrogio Cerri & Pierluigi Donia & Azzurra Massimi & Carolina Marzuillo & Corrado De Vito & Leandro Casini & Antonella Polimeni & Eugenio Gaudio, 2022. "Predictors of SARS-CoV-2 Infection in University Students: A Case-Control Study," IJERPH, MDPI, vol. 19(21), pages 1-13, November.

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