Variable selection in linear regression
Charles Lindsey () and
Simon Sheather
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Simon Sheather: Texas A&M University
Stata Journal, 2010, vol. 10, issue 4, 650-669
Abstract:
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.
Keywords: vselect; variable selection; regress; nestreg (search for similar items in EconPapers)
Date: 2010
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