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Variable selection in linear regression

  • Charles Lindsey

    ()

    (StataCorp)

  • Simon Sheather

    (Texas A&M University)

We present a new Stata program, vselect, that helps users perform variable selection after performing a linear regression. Options for stepwise meth- ods such as forward selection and backward elimination are provided. The user may specify Mallows’s Cp, Akaike’s information criterion, Akaike’s corrected informa- tion criterion, Bayesian information criterion, or R2 adjusted as the information criterion for the selection. When the user specifies the best subset option, the leaps-and-bounds algorithm (Furnival and Wilson, Technometrics 16: 499–511) is used to determine the best subsets of each predictor size. All the previously men- tioned information criteria are reported for each of these subsets. We also provide options for doing variable selection only on certain predictors (as in [R] nestreg) and support for weighted linear regression. All options are demonstrated on real datasets with varying numbers of predictors.

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Volume (Year): 10 (2010)
Issue (Month): 4 (December)
Pages: 650-669

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Handle: RePEc:tsj:stataj:v:10:y:2010:i:4:p:650-669
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