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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampMatos, Larissa Avila-
dc.typeArtigopt_BR
dc.titleHeavy-tailed longitudinal regression models for censored data : a robust parametric approachpt_BR
dc.contributor.authorMatos, Larissa A.-
dc.contributor.authorLachos, Víctor H.-
dc.contributor.authorTsung-I, Lin-
dc.subjectCarga viralpt_BR
dc.subjectDinâmica não linearpt_BR
dc.subjectAlgoritmo SAEMpt_BR
dc.subjectValores estranhos (Estatistica)pt_BR
dc.subject.otherlanguageViral loadpt_BR
dc.subject.otherlanguageNonlinear dynamicspt_BR
dc.subject.otherlanguageSAEM algorithmpt_BR
dc.subject.otherlanguageOutliers (Statistics)pt_BR
dc.description.abstractLongitudinal HIV-1 RNA viral load measures are often subject to censoring due to upper and lower detection limits depending on the quantification assays. A complication arises when these continuous measures present a heavy-tailed behavior because inference can be seriously affected by the misspecification of their parametric distribution. For such data structures, we propose a robust nonlinear censored regression model based on the scale mixtures of normal distributions. By taking into account the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is considered. A stochastic approximation of the EM algorithm is developed to obtain the maximum likelihood estimates of the model parameters. The main advantage of this new procedure os to allow estimating the parameters of interest and evaluating the log-likelihood function easily and quickly. Furthermore, the standard errors of the fixed effects and predictions of unobservable values of the response can be obtained as a byproduct. The practical utility of the proposed method is exemplified using both simulated and real datapt_BR
dc.relation.ispartofTestpt_BR
dc.publisher.cityHeidelbergpt_BR
dc.publisher.countryAlemanhapt_BR
dc.publisherSpringerpt_BR
dc.date.issued2019-09-
dc.date.monthofcirculationSept.pt_BR
dc.language.isoengpt_BR
dc.description.volume28pt_BR
dc.description.issuenumber3pt_BR
dc.description.firstpage844pt_BR
dc.description.lastpage878pt_BR
dc.rightsFechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn1133-0686pt_BR
dc.identifier.eissn1863-8260pt_BR
dc.identifier.doi10.1007/s11749-018-0603-5pt_BR
dc.identifier.urlhttps://link.springer.com/article/10.1007/s11749-018-0603-5pt_BR
dc.description.sponsorshipCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQpt_BR
dc.description.sponsorshipFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPpt_BR
dc.description.sponsordocumentnumber305054/2011-2pt_BR
dc.description.sponsordocumentnumber2011/22063-9; 2015/05385-3; 2014/ 02938-9; 2018/05013-7pt_BR
dc.date.available2020-03-18T19:08:14Z-
dc.date.accessioned2020-03-18T19:08:14Z-
dc.description.provenanceSubmitted by Susilene Barbosa da Silva (susilene@unicamp.br) on 2020-03-18T19:08:14Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-07-20T14:18:54Z : No. of bitstreams: 1 000482472000014.pdf: 1027218 bytes, checksum: 83bb1690db64815207045ee661b2d9b7 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-03-18T19:08:14Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-09en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/336843-
dc.contributor.departmentDepartamento de Estatísticapt_BR
dc.contributor.unidadeInstituto de Matemática, Estatística e Computação Científicapt_BR
dc.subject.keywordlongitudinal datapt_BR
dc.identifier.source000482472000014pt_BR
dc.creator.orcid0000-0002-2635-0901pt_BR
dc.type.formArtigo originalpt_BR
dc.description.sponsorNoteWe are grateful to two anonymous referees and the associate editor for very useful comments and suggestions, which greatly improved this paper. We also acknowledge the support from FAPESP-Brazil (Grants 2011/22063-9, 2015/05385-3, 2014/ 02938-9 and 2018/05013-7), CNPq-Brazil (Grant 305054/2011-2), Grant FONDECYT 1170258 from the Chilean government and the Ministry of Science and Technology of Taiwan (Grant MOST105-2118-M-005-003-MY2)pt_BR
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