Variable selection in linear regression

We present a new Stata program, vselect, that helps users perform variable selection after performing a linear regression. Options for stepwise methods such as forward selection and backward elimination are provided. The user may specify Mallows’s Cp, Akaike’s information criterion, Akaike’s corrected information 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 mentioned 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.


Issue Date:
2010
Publication Type:
Journal Article
DOI and Other Identifiers:
st0213 (Other)
PURL Identifier:
http://purl.umn.edu/163399
Published in:
Stata Journal, Volume 10, Number 4
Page range:
650-669
Total Pages:
20

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 Record created 2017-04-01, last modified 2017-08-27

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