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dc.contributor.authorDonnet, Sophie
HAL ID: 14568
ORCID: 0000-0003-4370-7316
dc.contributor.authorRivoirard, Vincent
dc.contributor.authorRousseau, Judith
dc.contributor.authorScricciolo, Catia
dc.date.accessioned2014-08-26T13:49:24Z
dc.date.available2014-08-26T13:49:24Z
dc.date.issued2017
dc.identifier.issn1931-6690
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/13828
dc.language.isoenen
dc.subjectposterior concentration rates
dc.subjectcounting processes
dc.subjectAalen model
dc.subjectDirichlet process mixtures
dc.subject.ddc519en
dc.titlePosterior concentration rates for counting processes with Aalen multiplicative intensities
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherDipartimento di Scienze delle Decision Bocconi University;Italie
dc.contributor.editoruniversityotherCentre de Recherche en Économie et Statistique (CREST) http://www.crest.fr/ INSEE – École Nationale de la Statistique et de l'Administration Économique;France
dc.contributor.editoruniversityotherathématiques et Informatique Appliquées (MIA) http://www.agroparistech.fr/mia/ Institut national de la recherche agronomique (INRA) : UMR0518 – AgroParisTech;France
dc.description.abstractenWe provide general conditions to derive posterior concentration rates for Aalen counting processes. The conditions are designed to resemble those proposed in the literature for the problem of density estimation, so that existing results on density estimation can be adapted to the present setting. We apply the general theorem to some prior models including Dirichlet process mixtures of uniform densities to estimate monotone non-increasing intensities and log-splines.
dc.publisher.cityParisen
dc.relation.isversionofjnlnameBayesian Analysis
dc.relation.isversionofjnlvol12
dc.relation.isversionofjnlissue1
dc.relation.isversionofjnldate2017
dc.relation.isversionofjnlpages53-87
dc.relation.isversionofdoi10.1214/15-BA986
dc.identifier.urlsitehttps://arxiv.org/abs/1407.6033v1
dc.relation.isversionofjnlpublisherInternational Society for Bayesian Analysis
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.description.submittednonen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2018-07-26T12:05:27Z


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