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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampLachos Dávila, Víctor Hugo-
dc.typeArtigopt_BR
dc.titleRobust linear mixed models with skew-normal independent distributions from a bayesian perspectivept_BR
dc.contributor.authorLachos, Victor H.-
dc.contributor.authorDey, Dipak K.-
dc.contributor.authorCancho, Vicente G.-
dc.subjectDistribuição normal assimétricapt_BR
dc.subject.otherlanguageSkew-normal distributionspt_BR
dc.description.abstractLinear mixed models were developed to handle clustered data and have been a topic of increasing interest in statistics for the past 50 years. Generally, the normality (or symmetry) of the random effects is a common assumption in linear mixed models but it may, sometimes, be unrealistic, obscuring important features of among-subjects variation. In this article, we utilize skew-normal/independent distributions as a tool for robust modeling of linear mixed models under a Bayesian paradigm. The skew-normal/independent distributions is an attractive class of asymmetric heavy-tailed distributions that includes the skew-normal distribution, skew-, skew-slash and the skew-contaminated normal distributions as special cases, providing an appealing robust alternative to the routine use of symmetric distributions in this type of models. The methods developed are illustrated using a real data set from Framingham cholesterol studypt_BR
dc.relation.ispartofJournal of statistical planning and inferencept_BR
dc.relation.ispartofabbreviationJ. stat. plan. inferencept_BR
dc.publisher.cityAmsterdam pt_BR
dc.publisher.countryPaíses Baixospt_BR
dc.publisherElsevierpt_BR
dc.date.issued2009-
dc.date.monthofcirculationDec.pt_BR
dc.language.isoengpt_BR
dc.description.volume139pt_BR
dc.description.issuenumber12pt_BR
dc.description.firstpage4098pt_BR
dc.description.lastpage4110pt_BR
dc.rightsFechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn0378-3758pt_BR
dc.identifier.eissn1873-1171pt_BR
dc.identifier.doi10.1016/j.jspi.2009.05.040pt_BR
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0378375809001669pt_BR
dc.description.sponsorshipCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQpt_BR
dc.description.sponsordocumentnumberSem informaçãopt_BR
dc.date.available2020-08-31T12:49:00Z-
dc.date.accessioned2020-08-31T12:49:00Z-
dc.description.provenanceSubmitted by Thais de Brito Barroso (tbrito@unicamp.br) on 2020-08-31T12:49:00Z No. of bitstreams: 0. Added 1 bitstream(s) on 2021-01-04T15:14:39Z : No. of bitstreams: 1 000270316000013.pdf: 523962 bytes, checksum: 5c1cd5c1a712d31c453608d35e33a208 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-08-31T12:49:00Z (GMT). No. of bitstreams: 0 Previous issue date: 2009en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/348327-
dc.contributor.departmentDepartamento de Estatísticapt_BR
dc.contributor.unidadeInstituto de Matemática, Estatística e Computação Científicapt_BR
dc.subject.keywordGibbs algorithmspt_BR
dc.subject.keywordLinear mixed modelspt_BR
dc.subject.keywordMCMCpt_BR
dc.subject.keywordMetropolis–hastingspt_BR
dc.subject.keywordSkew-normal/independent distributionpt_BR
dc.identifier.source000270316000013pt_BR
dc.creator.orcid0000-0002-7239-2459pt_BR
dc.type.formArtigopt_BR
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