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Jovanovic, Borko D.; Hosmer, David W.; Buonaccorsi, John P.

Equivalence of several methods for efficient best subsets selection in generalized linear models. (English) Zbl 0875.62337

Comput. Stat. Data Anal. 20, No. 1, 59-64 (1995).
Summary: In the recent past, five methods for reducing computational intensity in best subset selection for Generalized Linear Models (GLM) have been proposed. We review these methods and explicitly show their mutual equivalence. Further, we show how the existing linear regression software can be used for such efficient best subset selection. Using the summary results presented in this paper, efficient best subset selection can easily be made available for all nonlinear GLM already present in statistical packages. This is of special importance for computing environments where computational efficiency has a high priority.

Cited in 3 Documents

MSC:

62J12 Generalized linear models (logistic models)
62F07 Statistical ranking and selection procedures
65C99 Probabilistic methods, stochastic differential equations

Keywords:

Linearization; Logistic regression; Poisson regression; Survival models; Mallows’ C; Pseudo data

Software:

SAS/STAT
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Full Text: DOI

References:

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