A semiparametric Bayesian approach to extreme value estimation

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dc.contributor.authorNascimento, Fernando Ferraz do
dc.contributor.authorGamerman, Dani
dc.contributor.authorHEDIBERT FREITAS LOPES
dc.coverage.cidadeNão informadopt_BR
dc.coverage.paisNão Informadopt_BR
dc.creatorNascimento, Fernando Ferraz do
dc.creatorGamerman, Dani
dc.date.accessioned2022-08-18T17:38:44Z
dc.date.available2022-08-18T17:38:44Z
dc.date.issued2012
dc.description.otherThis paper is concerned with extreme value density estimation. The generalized Pareto distribution (GPD) beyond a given threshold is combined with a nonparametric estimation approach below the threshold. This semiparametric setup is shown to generalize a few existing approaches and enables density estimation over the complete sample space. Estimation is performed via the Bayesian paradigm, which helps identify model components. Estimation of all model parameters, including the threshold and higher quantiles, and prediction for future observations is provided. Simulation studies suggest a few useful guidelines to evaluate the relevance of the proposed procedures. They also provide empirical evidence about the improvement of the proposed methodology over existing approaches. Models are then applied to environmental data sets. The paper is concluded with a few directions for future work.pt_BR
dc.format.extentp. 661–675pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.doi10.1007/s11222-011-9270-zpt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4041
dc.identifier.volume22pt_BR
dc.language.isoInglêspt_BR
dc.publisherSpringerpt_BR
dc.relation.ispartofStatistics and Computingpt_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.keywordsGPDpt_BR
dc.subject.keywordsHigher quantilespt_BR
dc.subject.keywordsMCMCpt_BR
dc.subject.keywordsThreshold estimationpt_BR
dc.subject.keywordsNonparametric estimation of curvespt_BR
dc.titleA semiparametric Bayesian approach to extreme value estimationpt_BR
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://link.springer.com/article/10.1007/s11222-011-9270-z
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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