Similarity analysis in Bayesian random partition models

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dc.contributor.authorNavarrete, Carlos A.
dc.contributor.authorQuintana, Fernando A.
dc.date.accessioned2024-01-10T14:21:39Z
dc.date.available2024-01-10T14:21:39Z
dc.date.issued2011
dc.description.abstractThis work proposes a method to assess the influence of individual observations in the clustering generated by any process that involves random partitions. We call it Similarity Analysis. It basically consists of decomposing the estimated similarity matrix into an intrinsic and an extrinsic part, coupled with a new approach for representing and interpreting partitions. Individual influence is associated with the particular ordering induced by individual covariates, which in turn provides an interpretation of the underlying clustering mechanism. We present applications in the context of Species Sampling Mixture Models (SSMMs), including Bayesian density estimation and dependent linear regression models. (C) 2010 Elsevier B.V. All rights reserved.
dc.description.funderFONDECYT
dc.description.funderLaboratorio de Analisis Estocastico
dc.fechaingreso.objetodigital2024-04-09
dc.format.extent13 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.csda.2010.05.014
dc.identifier.eissn1872-7352
dc.identifier.issn0167-9473
dc.identifier.urihttps://doi.org/10.1016/j.csda.2010.05.014
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/79743
dc.identifier.wosidWOS:000283017900009
dc.information.autorucMatemática;Quintana F ;S/I;100343
dc.issue.numero1
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final109
dc.pagina.inicio97
dc.publisherELSEVIER
dc.revistaCOMPUTATIONAL STATISTICS & DATA ANALYSIS
dc.rightsacceso restringido
dc.subjectCluster analysis
dc.subjectRandom partitions
dc.subjectDirichlet process
dc.subjectPoisson-Dirichlet process
dc.subjectBayesian density estimation
dc.subjectDENSITY-ESTIMATION
dc.subjectDIRICHLET
dc.subjectINFERENCE
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleSimilarity analysis in Bayesian random partition models
dc.typeartículo
dc.volumen55
sipa.codpersvinculados100343
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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