Partially linear censored regression models using heavy-tailed distributions: A Bayesian approach

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dc.contributor.authorCastro, Luis M.
dc.contributor.authorLachos, Victor H.
dc.contributor.authorFerreira, Guillermo P.
dc.contributor.authorArellano Valle, Reinaldo B.
dc.date.accessioned2024-01-10T13:10:06Z
dc.date.available2024-01-10T13:10:06Z
dc.date.issued2014
dc.description.abstractLinear regression models where the response variable is censored are often considered in statistical analysis. A parametric relationship between the response variable and covariates and normality of random errors are assumptions typically considered in modeling censored responses. In this context, the aim of this paper is to extend the normal censored regression model by considering on one hand that the response variable is linearly dependent on some covariates whereas its relation to other variables is characterized by nonparametric functions, and on the other hand that error terms of the regression model belong to a class of symmetric heavy-tailed distributions capable of accommodating outliers and/or influential observations in a better way than the normal distribution. We achieve a fully Bayesian inference using pth-degree spline smooth functions to approximate the nonparametric functions. The likelihood function is utilized to compute not only some Bayesian model selection measures but also to develop Bayesian case-deletion influence diagnostics based on the q-divergence measures. The newly developed procedures are illustrated with an application and simulated data. (C) 2013 Elsevier B.V. All rights reserved.
dc.description.funderChilean government
dc.description.funderFundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP-Brazil)
dc.description.funderConselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq-Brazil)
dc.description.funderUniversidad de Concepcion
dc.fechaingreso.objetodigital11-04-2024
dc.format.extent18 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.stamet.2013.10.003
dc.identifier.eissn1878-0954
dc.identifier.issn1572-3127
dc.identifier.urihttps://doi.org/10.1016/j.stamet.2013.10.003
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/77771
dc.identifier.wosidWOS:000331856000002
dc.information.autorucFacultad de Matemáticas; Castro Cepero, Luis Mauricio; S/I; 151425
dc.language.isoen
dc.nota.accesoContenido parcial
dc.pagina.final31
dc.pagina.inicio14
dc.publisherELSEVIER SCIENCE BV
dc.revistaSTATISTICAL METHODOLOGY
dc.rightsacceso restringido
dc.subjectBayesian modeling
dc.subjectCensored regression models
dc.subjectNonlinear regression model
dc.subjectScale mixtures of normal distributions
dc.subjectSENSITIVITY
dc.subjectINFERENCE
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titlePartially linear censored regression models using heavy-tailed distributions: A Bayesian approach
dc.typeartículo
dc.volumen18
sipa.codpersvinculados151425
sipa.indexWOS
sipa.trazabilidadCarga SIPA;09-01-2024
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