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dc.contributor.authorChédin, Alain
dc.contributor.authorDiday, Edwin
dc.contributor.authorBillard, Lynne
dc.contributor.authorVrac, Mathieu
dc.date.accessioned2011-07-13T07:47:02Z
dc.date.available2011-07-13T07:47:02Z
dc.date.issued2012
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6678
dc.language.isoenen
dc.subjectMixture modelen
dc.subjectEstimationen
dc.subjectData distributionsen
dc.subjectDynamical clusteringen
dc.subjectCopulasen
dc.subjectClassification of distributionsen
dc.subject.ddc519en
dc.titleCopula analysis of mixture modelsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenContemporary computers collect databases that can be too large for classical methods to handle. The present work takes data whose observations are distribution functions (rather than the single numerical point value of classical data) and presents a computational statistical approach of a new methodology to group the distributions into classes. The clustering method links the searched partition to the decomposition of mixture densities, through the notions of a function of distributions and of multi-dimensional copulas. The new clustering technique is illustrated by ascertaining distinct temperature and humidity regions for a global climate dataset and shows that the results compare favorably with those obtained from the standard EM algorithm method.en
dc.relation.isversionofjnlnameComputational Statistics
dc.relation.isversionofjnlvol27
dc.relation.isversionofjnlissue3
dc.relation.isversionofjnldate2012
dc.relation.isversionofjnlpages427-457
dc.relation.isversionofdoihttp://dx.doi.org/10.1007/s00180-011-0266-0en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherSpringeren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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