Best subsets variable selection in nonnormal regression models
Charles Lindsey
StataCorp
College Station, TX
[email protected]
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Simon Sheather
Texas A&M Statistics
College Station, TX
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Abstract. We present a new program, gvselect, that helps users perform variable
selection in regression. Best subsets variable selection is performed and
provides the user with the best combinations of predictors for each level of
model complexity. The leaps-and-bounds (Furnival and Wilson, 1974,
Technometrics 16: 499–511) algorithm is applied using the log
likelihoods of candidate models. This allows the user to perform variable
selection on a wide variety of normal and nonnormal regression models. Our
method is described in Lawless and Singhal (1978, Biometrics 34:
318–327).
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Charles Lindsey, Simon Sheather
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gvselect, regress, vselect, variable selection
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