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dc.contributor.authorCeleux, Gilles
dc.contributor.authorForbes, Florence
dc.contributor.authorRobert, Christian P.
dc.contributor.authorTitterington, Mike
dc.date.accessioned2011-06-08T09:47:09Z
dc.date.available2011-06-08T09:47:09Z
dc.date.issued2006
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6448
dc.language.isoenen
dc.subjectrandom effect modelen
dc.subjectcompletionen
dc.subjectdevianceen
dc.subjectDICen
dc.subjectEM algorithmen
dc.subjectMAPen
dc.subjectmodel comparisonen
dc.subjectmixture modelen
dc.subject.ddc519en
dc.titleDeviance Information Criteria for Missing Data Modelsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThe 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.en
dc.relation.isversionofjnlnameBayesian Analysis
dc.relation.isversionofjnlvol1en
dc.relation.isversionofjnlissue4en
dc.relation.isversionofjnldate2006
dc.relation.isversionofjnlpages651-674en
dc.relation.isversionofdoihttp://dx.doi.org/10.1214/06-BA122en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherInternational Society for Bayesian Analysisen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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