Bayesian model comparison in generalized linear models across multiple groups

Tim Futing Liao

Research output: Contribution to journalArticlepeer-review


This paper extends the statistical method known as generalized linear Bayesian modeling developed by Adrian Raftery to the comparison of generalized linear models across multiple groups. The extension considers all relevant hierarchical models in the model space and tests parameter equality across groups by using Bayesian posterior information from the models. The conclusion drawn by using the proposed approach tends to be more conservative than Raftery's method and the conventional likelihood ratio test, as the examples demonstrates.

Original languageEnglish (US)
Pages (from-to)311-327
Number of pages17
JournalComputational Statistics and Data Analysis
Issue number3
StatePublished - May 28 2002


  • Bayesian statistics
  • Generalized linear models
  • Multiple group comparison

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


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