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

dc.creatorCastro, LM
dc.creatorLachos, VH
dc.creatorFerreira, GP
dc.creatorArellano-Valle, RB
dc.date2014
dc.dateMAY
dc.date2014-07-30T14:30:46Z
dc.date2015-11-26T16:33:10Z
dc.date2014-07-30T14:30:46Z
dc.date2015-11-26T16:33:10Z
dc.date.accessioned2018-03-28T23:14:54Z
dc.date.available2018-03-28T23:14:54Z
dc.identifierStatistical Methodology. Elsevier Science Bv, v. 18, n. 14, n. 31, 2014.
dc.identifier1572-3127
dc.identifier1878-0954
dc.identifierWOS:000331856000002
dc.identifier10.1016/j.stamet.2013.10.003
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/59091
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/59091
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1270754
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionLinear 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.description18
dc.description14
dc.description31
dc.descriptionChilean government [FONDECYT 1130233, FONDECYT 1120121]
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionUniversidad de Concepcion [DIUC 213.014.021-1.0]
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionChilean government [FONDECYT 1130233, FONDECYT 1120121]
dc.descriptionFAPESP [2012/19445-0, 2011/17400-6]
dc.descriptionCNPq [305054/2011-2]
dc.descriptionUniversidad de Concepcion [DIUC 213.014.021-1.0]
dc.languageen
dc.publisherElsevier Science Bv
dc.publisherAmsterdam
dc.publisherHolanda
dc.relationStatistical Methodology
dc.relationStat. Methodol.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectBayesian modeling
dc.subjectCensored regression models
dc.subjectNonlinear regression model
dc.subjectScale mixtures of normal distributions
dc.subjectMixed Models
dc.subjectPriors
dc.subjectSensitivity
dc.subjectInference
dc.subjectSelection
dc.subjectMixtures
dc.titlePartially linear censored regression models using heavy-tailed distributions: A Bayesian approach
dc.typeArtículos de revistas


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