Journal
BAYESIAN ANALYSIS
Volume 1, Issue 4, Pages 651-673Publisher
INT SOC BAYESIAN ANALYSIS
DOI: 10.1214/06-BA122
Keywords
completion; deviance; DIC; EM algorithm; MAP; model comparison; mixture model; random effect model
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The deviance information criterion (DIC) introduced by Spiegelhalter et al. (2002) for model assessment and model comparison is directly inspired by linear and generalised linear models, but it is open to different possible variations in the setting of missing data models, depending in particular on whether or not the missing variables are treated as parameters. In this paper, we reassess the criterion for such models and compare different DIC constructions, testing the behaviour of these various extensions in the cases of mixtures of distributions and random effect models.
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