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dc.contributor.authorCasarin, Roberto
dc.date.accessioned2011-05-26T12:26:41Z
dc.date.available2011-05-26T12:26:41Z
dc.date.issued2003
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6326
dc.language.isoenen
dc.subjectMixture modelen
dc.subjectStable distributionsen
dc.subjectGibbs samplingen
dc.subjectBayesian inferenceen
dc.subject.ddc519en
dc.subject.classificationjelC11en
dc.titleBayesian Inference for Mixtures of Stable Distributionsen
dc.typeCommunication / Conférence
dc.description.abstractenIn many different fields such as hydrology, telecommunications, physics of condensed matter and finance, the gaussian model results unsatisfactory and reveals difficulties in fitting data with skewness, heavy tails and multimodality. The use of stable distributions allows for modelling skewness and heavy tails but gives rise to inferential problems related to the estimation of the stable distributions' parameters. Some recent works have proposed characteristic function based estimation method and MCMC simulation based estimation techniques like the MCMC-EM method and the Gibbs sampling method in a full Bayesian approach. The aim of this work is to generalise the stable distribution framework by introducing a model that accounts also for multimodality. In particular we introduce a stable mixture model and a suitable reparametrisation of the mixture, which allow us to make inference on the mixture parameters. We use a full Bayesian approach and MCMC simulation techniques for the estimation of the posterior distribution. Finally we propose some applications of stable mixtures to financial data.en
dc.identifier.citationpages50en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.conftitleYoung Statistician Meetingen
dc.relation.confdate2003-04
dc.relation.confcityCambridgeen
dc.relation.confcountryRoyaume-Unien


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