Variable selection for Poisson regression model

Daniel Rothe, Kiya Felix Famoye

Research output: Contribution to journalArticlepeer-review

Abstract

Poisson regression is useful in modeling count data. In a study with many independent variables, it is desirable to reduce the number of variables while maintaining a model that is useful for prediction. This article presents a variable selection technique for Poisson regression models. The data used is log-linear, but the methods could be adapted to other relationships. The model parameters are estimated by the method of maximum likelihood. The use of measures of goodness-of-fit to select appropriate variables is discussed. A forward selection algorithm is presented and illustrated on a numerical data set. This algorithm performs as well if not better than the method of transformation proposed by Nordberg (1982).

Original languageEnglish
Pages (from-to)380-388
JournalJournal of Modern Applied Statistical Methods
Volume2
Issue number2
StatePublished - 2003

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