Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/356343
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampSchumacher, Fernanda Lang-
dc.typeArtigopt_BR
dc.titleA robust nonlinear mixed-effects model for COVID-19 death datapt_BR
dc.contributor.authorSchumacher, Fernanda L.-
dc.contributor.authorFerreira, Clécio S.-
dc.contributor.authorPrates, Marcos O.-
dc.contributor.authorLachos, Alberto-
dc.contributor.authorLachos, Victor H.-
dc.subjectCoronavíruspt_BR
dc.subjectAlgoritmospt_BR
dc.subject.otherlanguageCoronavirusespt_BR
dc.subject.otherlanguageAlgorithmspt_BR
dc.description.abstractThe analysis of complex longitudinal data such as COVID-19 deaths is challenging due to several inherent features: (i) similarly-shaped profiles with different decay patterns; (ii) unexplained variation among repeated measurements within each country, possibly interpreted as clustered data since they are obtained from the same country at roughly the same time; and (iii) skewness, outliers or skewed heavy-tailed noises possibly embodied within response variables. This article formulates a robust nonlinear mixed effects model based on the class of scale mixtures of skew-normal distributions to model COVID-19 deaths, which allows analysts to model such data in the presence of the above described features simultaneously. An efficient EM-type algorithm is proposed to carry out maximum likelihood estimation of model parameters. The bootstrap method is used to determine inherent characteristics of the individual nonlinear profiles, such as confidence intervals of the predicted deaths and fitted curves. The specific target is to model COVID-19 death curves from some Latin American countries since this region is the new epicenter of the disease. Moreover, since a mixed-effect framework borrows information from the population-average effects, in our analysis we include some countries from Europe and North America that are in a more advanced stage of the COVID-19 death curvept_BR
dc.relation.ispartofStatistics and its interfacept_BR
dc.publisher.citySomervillept_BR
dc.publisher.countryEstados Unidospt_BR
dc.publisherInternational Presspt_BR
dc.date.issued2021-
dc.language.isoengpt_BR
dc.description.volume14pt_BR
dc.description.issuenumber1pt_BR
dc.description.firstpage49pt_BR
dc.description.lastpage57pt_BR
dc.rightsFechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn1938-7989pt_BR
dc.identifier.eissn1938-7997pt_BR
dc.identifier.doi10.4310/20-SII637pt_BR
dc.identifier.urlhttps://www.intlpress.com/site/pub/pages/journals/items/sii/content/vols/0014/0001/a011/index.phppt_BR
dc.description.sponsorshipCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQpt_BR
dc.description.sponsorshipCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESpt_BR
dc.description.sponsordocumentnumberNão tempt_BR
dc.description.sponsordocumentnumber001pt_BR
dc.date.available2021-02-23T17:38:57Z-
dc.date.accessioned2021-02-23T17:38:57Z-
dc.description.provenanceSubmitted by Susilene Barbosa da Silva (susilene@unicamp.br) on 2021-02-23T17:38:57Z No. of bitstreams: 0en
dc.description.provenanceMade available in DSpace on 2021-02-23T17:38:57Z (GMT). No. of bitstreams: 0 Previous issue date: 2021en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/356343-
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.unidadeInstituto de Matemática, Estatística e Computação Científicapt_BR
dc.subject.keywordSkew-normal distributionspt_BR
dc.identifier.source000600100500012pt_BR
dc.creator.orcidSem informaçãopt_BR
dc.type.formArtigopt_BR
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