Random partition models with regression on covariates

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dc.contributor.authorMuellner, Peter
dc.contributor.authorQuintana, Fernando
dc.date.accessioned2024-01-10T13:17:00Z
dc.date.available2024-01-10T13:17:00Z
dc.date.issued2010
dc.description.abstractMany recent applications of nonparametric Bayesian inference use random partition models, i.e. probability models for clustering a set of experimental units. We review the popular basic constructions. We then focus on an interesting extension of such models. In many applications covariates are available that could be used to a priori inform the clustering. This leads to random clustering models indexed by covariates, i.e., regression models with the outcome being a partition of the experimental units. We discuss some alternative approaches that have been used in the recent literature to implement such models, with an emphasis on a recently proposed extension of product partition models. Several of the reviewed approaches were not originally intended as covariate-based random partition models, but can be used for such inference. (C) 2010 Elsevier B.V. All rights reserved.
dc.description.funderNIH/NCI
dc.description.funderFondo Nacional de Desarrollo Cientifico y Tecnologico Fondecyt
dc.description.funderNATIONAL CANCER INSTITUTE
dc.fechaingreso.objetodigital01-04-2024
dc.format.extent8 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.jspi.2010.03.002
dc.identifier.issn0378-3758
dc.identifier.pubmedidMEDLINE:20694040
dc.identifier.urihttps://doi.org/10.1016/j.jspi.2010.03.002
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/78630
dc.identifier.wosidWOS:000279092200002
dc.information.autorucMatemática;Quintana F;S/I;100343
dc.issue.numero10
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final2808
dc.pagina.inicio2801
dc.publisherELSEVIER SCIENCE BV
dc.revistaJOURNAL OF STATISTICAL PLANNING AND INFERENCE
dc.rightsacceso restringido
dc.subjectClustering
dc.subjectNon-parametric Bayes
dc.subjectProduct partition model
dc.subjectNONPARAMETRIC PROBLEMS
dc.subjectDIRICHLET PROCESSES
dc.subjectBAYESIAN-ANALYSIS
dc.subjectMIXTURES
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleRandom partition models with regression on covariates
dc.typeartículo
dc.volumen140
sipa.codpersvinculados100343
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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