A semiparametric Bayesian approach to extreme value estimation

This 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 e...

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Detalles Bibliográficos
Autores: Nascimento, Fernando Ferraz do, Gamerman, Dani, HEDIBERT FREITAS LOPES
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2012
País:Brasil
Institución:Instituição de Ensino Superior e de Pesquisa (INSPER)
Repositorio:Repositório Institucional da INSPER
Idioma:inglés
OAI Identifier:oai:repositorio.insper.edu.br:11224/4041
Acceso en línea:https://repositorio.insper.edu.br/handle/11224/4041
Access Level:acceso abierto
Palabra clave:Bayesian
GPD
Higher quantiles
MCMC
Threshold estimation
Nonparametric estimation of curves
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spelling A semiparametric Bayesian approach to extreme value estimationBayesianGPDHigher quantilesMCMCThreshold estimationNonparametric estimation of curvesThis 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.Springer2022-08-18T17:38:44Z2022-08-18T17:38:44Z2012info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlep. 661–675Digitalapplication/pdfapplication/pdfhttps://repositorio.insper.edu.br/handle/11224/404110.1007/s11222-011-9270-z22Statistics and ComputingNão InformadoNão informadoO 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.info:eu-repo/semantics/openAccessengreponame:Repositório Institucional da INSPERinstname:Instituição de Ensino Superior e de Pesquisa (INSPER)instacron:INSPERNascimento, Fernando Ferraz doGamerman, DaniNascimento, Fernando Ferraz doGamerman, DaniHEDIBERT FREITAS LOPES2025-08-26T16:04:29Zoai:repositorio.insper.edu.br:11224/4041Biblioteca Digital de Teses e Dissertaçõeshttps://www.insper.edu.br/biblioteca-telles/PRIhttps://repositorio.insper.edu.br/oai/requestbiblioteca@insper.edu.br || conteudobiblioteca@insper.edu.bropendoar:2025-08-26T16:04:29Repositório Institucional da INSPER - Instituição de Ensino Superior e de Pesquisa (INSPER)false
dc.title.none.fl_str_mv A semiparametric Bayesian approach to extreme value estimation
title A semiparametric Bayesian approach to extreme value estimation
spellingShingle A semiparametric Bayesian approach to extreme value estimation
Nascimento, Fernando Ferraz do
Bayesian
GPD
Higher quantiles
MCMC
Threshold estimation
Nonparametric estimation of curves
title_short A semiparametric Bayesian approach to extreme value estimation
title_full A semiparametric Bayesian approach to extreme value estimation
title_fullStr A semiparametric Bayesian approach to extreme value estimation
title_full_unstemmed A semiparametric Bayesian approach to extreme value estimation
title_sort A semiparametric Bayesian approach to extreme value estimation
dc.creator.none.fl_str_mv Nascimento, Fernando Ferraz do
Gamerman, Dani
Nascimento, Fernando Ferraz do
Gamerman, Dani
HEDIBERT FREITAS LOPES
author Nascimento, Fernando Ferraz do
author_facet Nascimento, Fernando Ferraz do
Gamerman, Dani
HEDIBERT FREITAS LOPES
author_role author
author2 Gamerman, Dani
HEDIBERT FREITAS LOPES
author2_role author
author
dc.subject.por.fl_str_mv Bayesian
GPD
Higher quantiles
MCMC
Threshold estimation
Nonparametric estimation of curves
topic Bayesian
GPD
Higher quantiles
MCMC
Threshold estimation
Nonparametric estimation of curves
description This 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.
publishDate 2012
dc.date.none.fl_str_mv 2012
2022-08-18T17:38:44Z
2022-08-18T17:38:44Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://repositorio.insper.edu.br/handle/11224/4041
10.1007/s11222-011-9270-z
22
url https://repositorio.insper.edu.br/handle/11224/4041
identifier_str_mv 10.1007/s11222-011-9270-z
22
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Statistics and Computing
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv p. 661–675
Digital
application/pdf
application/pdf
dc.coverage.none.fl_str_mv Não Informado
Não informado
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositório Institucional da INSPER
instname:Instituição de Ensino Superior e de Pesquisa (INSPER)
instacron:INSPER
instname_str Instituição de Ensino Superior e de Pesquisa (INSPER)
instacron_str INSPER
institution INSPER
reponame_str Repositório Institucional da INSPER
collection Repositório Institucional da INSPER
repository.name.fl_str_mv Repositório Institucional da INSPER - Instituição de Ensino Superior e de Pesquisa (INSPER)
repository.mail.fl_str_mv biblioteca@insper.edu.br || conteudobiblioteca@insper.edu.br
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score 15.228081

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