Semiparametric Bayesian measurement error modeling

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dc.contributor.authorCasanova, Maria P.
dc.contributor.authorIglesias, Pilar
dc.contributor.authorBolfarine, Heleno
dc.contributor.authorSalinas, Victor H.
dc.contributor.authorPena, Alexis
dc.date.accessioned2024-01-10T12:08:29Z
dc.date.available2024-01-10T12:08:29Z
dc.date.issued2010
dc.description.abstractThis work presents a Bayesian semiparametric approach for dealing with regression models where the covariate is measured with error. Given that (1) the error normality assumption is very restrictive, and (2) assuming a specific elliptical distribution for errors (Student-t for example), may be somewhat presumptuous; there is need for more flexible methods, in terms of assuming only symmetry of errors (admitting unknown kurtosis). In this sense, the main advantage of this extended Bayesian approach is the possibility of considering generalizations of the elliptical family of models by using Dirichlet process priors in dependent and independent situations. Conditional posterior distributions are implemented, allowing the use of Markov Chain Monte Carlo (MCMC), to generate the posterior distributions. An interesting result shown is that the Dirichlet process prior is not updated in the case of the dependent elliptical model. Furthermore, an analysis of a real data set is reported to illustrate the usefulness of our approach, in dealing with outliers. Finally, semiparametric proposed models and parametric normal model are compared, graphically with the posterior distribution density of the coefficients. (C) 2009 Elsevier Inc. All rights reserved.
dc.description.funderDIUC UDEC
dc.description.funderFONDECYT
dc.fechaingreso.objetodigital17-04-2024
dc.format.extent13 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.jmva.2009.11.004
dc.identifier.issn0047-259X
dc.identifier.urihttps://doi.org/10.1016/j.jmva.2009.11.004
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/76397
dc.identifier.wosidWOS:000273834800003
dc.information.autorucMatemática;Iglesias P;S/I;100265
dc.information.autorucMatemática;Peña A;S/I;111511
dc.issue.numero3
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final524
dc.pagina.inicio512
dc.publisherELSEVIER INC
dc.revistaJOURNAL OF MULTIVARIATE ANALYSIS
dc.rightsacceso restringido
dc.subjectClassical measurement error model
dc.subjectHierarchical elliptical model
dc.subjectPosterior distribution
dc.subjectDirichlet process
dc.subjectGibbs sampling
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleSemiparametric Bayesian measurement error modeling
dc.typeartículo
dc.volumen101
sipa.codpersvinculados100265
sipa.codpersvinculados111511
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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