Regression models for exceedance data via the full likelihood

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dc.contributor.authorHEDIBERT FREITAS LOPES
dc.contributor.authorNascimento, Fernando Ferraz do
dc.contributor.authorGamerman, Dani
dc.coverage.cidadeNão informadopt_BR
dc.coverage.paisNão Informadopt_BR
dc.creatorNascimento, Fernando Ferraz do
dc.creatorGamerman, Dani
dc.date.accessioned2022-10-04T20:22:52Z
dc.date.available2022-10-04T20:22:52Z
dc.date.issued2011
dc.description.otherMany situations in practice require appropriate specification of operating characteristics under extreme conditions. Typical examples include environmental sciences where studies include extreme temperature, rainfall and river flow to name a few. In these cases, the effect of geographic and climatological inputs are likely to play a relevant role. This paper is concerned with the study of extreme data in the presence of relevant auxiliary information. The underlying model involves a mixture distribution: a generalized Pareto distribution is assumed for the exceedances beyond a high threshold and a non-parametric approach is assumed for the data below the threshold. Thus, the full likelihood including data below and above the threshold is considered in the estimation. The main novelty is the introduction of a regression struc ture to explain the variation of the exceedances through all tail parameters. Estimation is performed under the Bayesian paradigm and includes model choice. This allows for determination of higher quantiles under each covariate configuration and upper bounds for the data, where appropriate. Simulation results show that the models are appropriate and identifiable. The models are applied to the study of two temperature datasets: maxima in the U.S.A. and minima in Brazil, and compared to other related modelspt_BR
dc.format.extentp. 495-512pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.doi10.1007/s10651-010-0148-6pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4130
dc.identifier.volume18pt_BR
dc.language.isoInglêspt_BR
dc.publisherNão informadopt_BR
dc.relation.ispartofEnviron Ecol Statpt_BR
dc.rights.licenseO INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR.pt_BR
dc.subject.keywordsBayesianpt_BR
dc.subject.keywordsGeneralized Pareto distributionpt_BR
dc.subject.keywordsHierarchical modelspt_BR
dc.subject.keywordsHigher quantilespt_BR
dc.subject.keywordsMCMCpt_BR
dc.subject.keywordsMixture of distributionspt_BR
dc.titleRegression models for exceedance data via the full likelihoodpt_BR
dc.typejournal article
dspace.entity.typePublication
local.subject.cnpqCiências Sociais Aplicadaspt_BR
local.typeArtigo Científicopt_BR
relation.isAuthorOfPublication41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
relation.isAuthorOfPublication.latestForDiscovery41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca

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