Deviance Information Criteria for Missing Data Models
Celeux, Gilles; Forbes, Florence; Robert, Christian P.; Titterington, Mike (2006), Deviance Information Criteria for Missing Data Models, Bayesian Analysis, 1, 4, p. 651-674. http://dx.doi.org/10.1214/06-BA122
TypeArticle accepté pour publication ou publié
Journal nameBayesian Analysis
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Abstract (EN)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.
Subjects / Keywordsrandom effect model; completion; deviance; DIC; EM algorithm; MAP; model comparison; mixture model
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