Model selection criteria for loglinear models

E. J. Bedrick, W. K. Crandall

Research output: Contribution to journalArticlepeer-review

Abstract

Considerable work has been devoted to developing model selection criteria for normal theory regression models. Less attention has been paid to discrete data. We develop two loglinear model selection criteria for Poisson counts. These criteria are based on an estimated bias adjustment of the Akaike information criterion. We observe in a simulation study that the corrected statistics provide good model choices and relatively accurate estimates of the mean structure.

Original languageEnglish (US)
Pages (from-to)439-449
Number of pages11
JournalAustralian and New Zealand Journal of Statistics
Volume52
Issue number4
DOIs
StatePublished - Dec 2010
Externally publishedYes

Keywords

  • AIC
  • BIC
  • Contingency table
  • Multinomial model
  • Poisson counts

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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