Bayesian clustering and product partition models

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dc.contributor.authorQuintana, FA
dc.contributor.authorIglesias, PL
dc.date.accessioned2024-01-10T12:37:46Z
dc.date.available2024-01-10T12:37:46Z
dc.date.issued2003
dc.description.abstractWe present a decision theoretic formulation of product partition models (PPMs) that allows a formal treatment of different decision problems such as estimation or hypothesis testing and clustering methods simultaneously. A key observation in our construction is the fact that PPMs can be formulated in the context of model selection. The underlying partition structure in these models is closely related to that arising in connection with Dirichlet processes. This allows a straightforward adaptation of some computational strategies-originally devised for nonparametric Bayesian problems-to our framework. The resulting algorithms are more flexible than other competing alternatives that are used for problems involving PPMs. We propose an algorithm that yields Bayes estimates of the quantities of interest and the groups of experimental units. We explore the application of our methods to the detection of outliers in normal and Student t regression models, with clustering structure equivalent to that induced by, a Dirichlet process prior. We also discuss the sensitivity of the results considering different prior distributions for the partitions.
dc.fechaingreso.objetodigital2024-05-28
dc.format.extent18 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1111/1467-9868.00402
dc.identifier.issn1369-7412
dc.identifier.urihttps://doi.org/10.1111/1467-9868.00402
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/76919
dc.identifier.wosidWOS:000182986800014
dc.information.autorucMatemática;Iglesias P;S/I;100265
dc.information.autorucMatemática;Quintana F;S/I;100343
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final574
dc.pagina.inicio557
dc.publisherBLACKWELL PUBL LTD
dc.revistaJOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
dc.rightsacceso restringido
dc.subjectclustering algorithm
dc.subjectDirichlet process prior
dc.subjectk-means algor ithm
dc.subjectoutlier detection
dc.subjectsharp model
dc.subjectNONPARAMETRIC PROBLEMS
dc.subjectDISTRIBUTIONS
dc.subjectCONVERGENCE
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleBayesian clustering and product partition models
dc.typeartículo
dc.volumen65
sipa.codpersvinculados100265
sipa.codpersvinculados100343
sipa.indexWOS
sipa.trazabilidadCarga SIPA;09-01-2024
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