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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.typeArtigo de periódicopt_BR
dc.titleEstimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributionspt_BR
dc.contributor.authorLabra, Filidor V.pt_BR
dc.contributor.authorGaray, Aldo M.pt_BR
dc.contributor.authorLachos, Victor H.pt_BR
dc.contributor.authorOrtega, Edwin M. M.pt_BR
unicamp.authorLabra, Filidor V.pt_BR
unicamp.authorGaray, Aldo M.pt_BR
unicamp.authorLachos, Victor H.pt_BR
unicamp.author.externalOrtega, Edwin M. M.pt
dc.subjectCase-deletion modelpt_BR
dc.subjectEM algorithmpt_BR
dc.subjectHomogeneitypt_BR
dc.subjectLocal influencept_BR
dc.subjectNonlinear regression modelspt_BR
dc.subjectScale mixtures of skew-normal distributionspt_BR
dc.subject.wosLOCAL INFLUENCEpt_BR
dc.subject.wosMAXIMUM-LIKELIHOODpt_BR
dc.subject.wosINCOMPLETE-DATApt_BR
dc.subject.wosLINEAR-MODELSpt_BR
dc.description.abstractAn extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. This novel class of models provides a useful generalization of the heteroscedastic symmetrical nonlinear regression models (Cysneiros et al., 2010), since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions such as skew-t, skew-slash, skew-contaminated normal, among others. A simple EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters is presented and the observed information matrix is derived analytically. In order to examine the performance of the proposed methods, some simulation studies are presented to show the robust aspect of this flexible class against outlying and influential observations and that the maximum likelihood estimates based on the EM-type algorithm do provide good asymptotic properties. Furthermore, local influence measures and the one-step approximations of the estimates in the case-deletion model are obtained. Finally, an illustration of the methodology is given considering a data set previously analyzed under the homoscedastic skew-t nonlinear regression model. (C) 2012 Elsevier B.V. All rights reserved.pt
dc.relation.ispartofJournal of Statistical Planning and Inferencept_BR
dc.publisher.cityAmsterdampt_BR
dc.publisherElsevierpt_BR
dc.date.issued2012pt_BR
dc.identifier.citationJournal of Statistical Planning and Inference. Elsevier, v.142, n.7, p.2149-2165, 2012pt_BR
dc.language.isoengpt_BR
dc.description.volume142pt_BR
dc.description.issuenumber7pt_BR
dc.description.firstpage2149pt_BR
dc.description.lastpage2165pt_BR
dc.rightsfechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn0378-3758pt_BR
dc.identifier.wosidWOS:000304074500045pt_BR
dc.identifier.doi10.1016/j.jspi.2012.02.018pt_BR
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)pt_BR
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt_BR
dc.description.sponsorship1Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)pt_BR
dc.description.sponsorship1Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt_BR
dc.date.available2013-09-19T18:06:42Z
dc.date.available2016-06-30T18:26:39Z-
dc.date.accessioned2013-09-19T18:06:42Z
dc.date.accessioned2016-06-30T18:26:39Z-
dc.description.provenanceMade available in DSpace on 2013-09-19T18:06:42Z (GMT). No. of bitstreams: 0 Previous issue date: 2012en
dc.description.provenanceMade available in DSpace on 2016-06-30T18:26:39Z (GMT). No. of bitstreams: 0 Previous issue date: 2012en
dc.identifier.urihttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/2379
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/2379-
dc.contributor.departmentEstatística
dc.contributor.unidadeIMECCpt
Appears in Collections:IMECC - Artigos e Outros Documentos

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