The Dependent Dirichlet Process and Related Models

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dc.catalogadoraba
dc.contributor.authorQuintana Quintana, Fernando
dc.contributor.authorMuller, Peter
dc.contributor.authorJara Vallejos, Alejandro Antonio
dc.contributor.authorMacEachern, Steven N.
dc.date.accessioned2024-06-25T21:34:42Z
dc.date.available2024-06-25T21:34:42Z
dc.date.issued2022
dc.description.abstractStandard regression approaches assume that some finite number of the response distribution characteristics, such as location and scale, change as a (parametric or nonparametric) function of predictors. However, it is not always appropriate to assume a location/scale representation, where the error distribution has unchanging shape over the predictor space. In fact, it often happens in applied research that the distribution of responses under study changes with predictors in ways that cannot be reasonably represented by a finite dimensional functional form. This can seriously affect the answers to the scientific questions of interest, and therefore more general approaches are indeed needed. This gives rise to the study of fully nonparametric regression models. We review some of the main Bayesian approaches that have been employed to define probability models where the complete response distribution may vary flexibly with predictors. We focus on developments based on modifications of the Dirichlet process, historically termed dependent Dirichlet processes, and some of the extensions that have been proposed to tackle this general problem using nonparametric approaches.
dc.description.funderANID-Millennium Science Initiative Program
dc.description.funderFondecyt
dc.description.funderNational Science Foundation
dc.description.funderU.S. National Cancer Institute
dc.format.extent18 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1214/20-STS819
dc.identifier.eissn2168-8745
dc.identifier.issn0883-4237
dc.identifier.scopusid2-s2.0-85124165784
dc.identifier.urihttps://doi.org/10.1214/20-STS819
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86855
dc.identifier.wosidWOS:000745960400002
dc.information.autorucFacultad de Matemáticas; Quintana Quintana, Fernando; S/I; 100343
dc.information.autorucFacultad de Matemáticas; Jara Vallejos, Alejandro Antonio; 0000-0002-2282-353X; 127927
dc.issue.numero1
dc.language.isoen
dc.pagina.final41
dc.pagina.inicio24
dc.publisherINST MATHEMATICAL STATISTICS-IMS
dc.revistaSTATISTICAL SCIENCE
dc.rightsacceso restringido
dc.subjectRelated random probability distributions
dc.subjectBayesian nonparametrics
dc.subjectNonparametric regression
dc.subjectQuantile regression
dc.subject.ddc510
dc.subject.deweyMatemática física y químicaes_ES
dc.titleThe Dependent Dirichlet Process and Related Models
dc.typeartículo
dc.volumen37
sipa.codpersvinculados100343
sipa.codpersvinculados127927
sipa.trazabilidadWOS;18-03-2022
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