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Variable selection in linear regression

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Author Info

  • Charles Lindsey

    ()
    (StataCorp)

  • Simon Sheather

    (Texas A&M University)

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    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.

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    Bibliographic Info

    Article provided by StataCorp LP in its journal Stata Journal.

    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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    Related research

    Keywords: vselect; variable selection; regress; nestreg;

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    Cited by:
    1. Anwar, Sajid & Sun, Sizhong, 2012. "Trade liberalisation, market competition and wage inequality in China's manufacturing sector," Economic Modelling, Elsevier, vol. 29(4), pages 1268-1277.

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