Variable selection in linear regression
Charles Lindsey
StataCorp
College Station, TX
[email protected]
|
Simon Sheather
Department of Statistics
Texas A&M University
College Station, TX
|
Abstract. 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.
View all articles by these authors:
Charles Lindsey, Simon Sheather
View all articles with these keywords:
vselect, variable selection, regress, nestreg
Download citation: BibTeX RIS
Download citation and abstract: BibTeX RIS
|
