A predictive view of Bayesian clustering

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dc.contributor.authorQuintana, Fernando A.
dc.date.accessioned2024-01-10T12:04:18Z
dc.date.available2024-01-10T12:04:18Z
dc.date.issued2006
dc.description.abstractThis work considers probability models for partitions of a set of n elements using a predictive approach, i.e., models that are specified in terms of the conditional probability of either joining an already existing cluster or forming a new one. The inherent structure can be motivated by resorting to hierarchical models of either parametric or nonparametric nature. Parametric examples include the product partition models (PPMs) and the model-based approach of Dasgupta and Raftery (J. Amer. Statist. Assoc. 93 (1998) 294), while nonparametric alternatives include the Dirichlet process, and more generally, the species sampling models (SSMs). Under exchangeability, PPMs and SSMs induce the same type of partition structure. The methods are discussed in the context of outlier detection in normal linear regression models and of (univariate) density estimation. (c) 2004 Elsevier B.V. All rights reserved.
dc.fechaingreso.objetodigital01-04-2024
dc.format.extent23 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.jspi.2004.09.015
dc.identifier.eissn1873-1171
dc.identifier.issn0378-3758
dc.identifier.urihttps://doi.org/10.1016/j.jspi.2004.09.015
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/75753
dc.identifier.wosidWOS:000237678300001
dc.information.autorucMatemática;Quintana F;S/I;100343
dc.issue.numero8
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final2429
dc.pagina.inicio2407
dc.publisherELSEVIER
dc.revistaJOURNAL OF STATISTICAL PLANNING AND INFERENCE
dc.rightsacceso restringido
dc.subjectdensity estimation
dc.subjectDirichlet process
dc.subjectEM algorithm
dc.subjectmodel-based clustering
dc.subjectoutlier detection
dc.subjectproduct partition models
dc.subjectspecies sampling models
dc.subjectPRODUCT PARTITION MODELS
dc.subjectMAXIMUM-LIKELIHOOD
dc.subjectDENSITY-ESTIMATION
dc.subjectSAMPLING METHODS
dc.subjectUNKNOWN NUMBER
dc.subjectMIXTURE-MODELS
dc.subjectREGRESSION
dc.subjectOUTLIERS
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleA predictive view of Bayesian clustering
dc.typeartículo
dc.volumen136
sipa.codpersvinculados100343
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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