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dc.contributor.authorGassiat, Elisabeth
dc.contributor.authorRousseau, Judith
dc.date.accessioned2017-10-30T16:36:13Z
dc.date.available2017-10-30T16:36:13Z
dc.date.issued2016
dc.identifier.issn1350-7265
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/16872
dc.language.isoenen
dc.subjecttranslation mixturesen
dc.subjectnon parametric estimationen
dc.subjectsemi-parametricmodelsen
dc.subjectHidden Markov models,dependent latent variable modelsen
dc.subject.ddc519en
dc.titleNonparametric finite translation hidden Markov models and extensionsen
dc.title.alternativeNon parametric finite translation mixturesen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenIn this paper, we consider nonparametric finite translation hidden Markov models, or more generally finite translation mixtures with dependent latent variables. We prove that all the parameters of the model are identifiable as soon as the matrix that defines the joint distribution of two consecutive latent variables is non-singular and the translation parameters are distinct. Under this assumption, we provide a consistent estimator of the number of populations, of the translation parameters and of the distribution of two consecutive latent variables, which we prove to be asymptotically normally distributed under mild dependency assumptions. We propose a nonparametric estimator of the unknown translated density. In case the latent variables form a Markov chain, we prove that this estimator is minimax adaptive over regularity classes of densities.en
dc.relation.isversionofjnlnameBernoulli
dc.relation.isversionofjnlvol22en
dc.relation.isversionofjnlissue1en
dc.relation.isversionofjnldate2016
dc.relation.isversionofjnlpages193-212en
dc.relation.isversionofdoi10.3150/14-BEJ631en
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-00786750/documenten
dc.relation.isversionofjnlpublisherInternational Statistical Instituteen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2017-10-30T16:33:47Z
hal.person.labIds40
hal.person.labIds60


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