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dc.contributor.authorCeleux, Gilles
dc.contributor.authorMarin, Jean-Michel
HAL ID: 9121
ORCID: 0000-0001-7451-9719
dc.contributor.authorRobert, Christian P.
dc.date.accessioned2011-05-09T09:21:05Z
dc.date.available2011-05-09T09:21:05Z
dc.date.issued2006
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6215
dc.language.isoenen
dc.subjectAdaptive algorithmsen
dc.subjectBayesian inferenceen
dc.subjectLatent variable modelsen
dc.subjectPopulation Monte Carloen
dc.subjectRao–Blackwellisationen
dc.subjectStochastic volatility modelen
dc.subject.ddc519en
dc.subject.classificationjelC15en
dc.titleIterated importance sampling in missing data problemsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenMissing variable models are typical benchmarks for new computational techniques in that the ill-posed nature of missing variable models offer a challenging testing ground for these techniques. This was the case for the EM algorithm and the Gibbs sampler, and this is also true for importance sampling schemes. A population Monte Carlo scheme taking advantage of the latent structure of the problem is proposed. The potential of this approach and its specifics in missing data problems are illustrated in settings of increasing difficulty, in comparison with existing approaches. The improvement brought by a general Rao–Blackwellisation technique is also discussed.en
dc.relation.isversionofjnlnameComputational Statistics and Data Analysis
dc.relation.isversionofjnlvol50en
dc.relation.isversionofjnlissue12en
dc.relation.isversionofjnldate2006
dc.relation.isversionofjnlpages3386-3404en
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.csda.2005.07.018en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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