Extended generalized extreme value distribution with applications in environmental data

Detalhes bibliográficos
Autor(a) principal: Nascimento, Fernando
Data de Publicação: 2015
Outros Autores: Bourguignon, Marcelo, Leão, Jeremias
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRN
Texto Completo: https://repositorio.ufrn.br/handle/123456789/49657
Resumo: In probability theory and statistics, the generalized extreme value (GEV) distribution is a family of continuous probability distributions developed within extreme value theory, which has wide applicability in several areas including hydrology, engineering, science, ecology and finance. In this paper, we propose three extensions of the GEV distribution that incorporate an additional parameter. These extensions are more flexible than the GEV distribution, i.e., the additional parameter introduces skewness and to vary tail weight. In these three cases, the GEV distribution is a particular case. The parameter estimation of these new distributions is done under the Bayesian paradigm, considering vague priors for the parameters. Simulation studies show the efficiency of the proposed models. Applications to river quotas and rainfall show that the generalizations can produce more efficient results than is the standard case with GEV distribution.
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spelling Nascimento, FernandoBourguignon, MarceloLeão, Jeremias2022-10-31T21:14:05Z2022-10-31T21:14:05Z2015NASCIMENTO, F. F.; BOURGUIGNON, Marcelo; LEÃO, Jeremias. Extended generalized extreme value distribution with applications in environmental data. Hacettepe Journal of Mathematics and Statistics , v. 46, p. 1-1, 2015. Disponível em: http://www.hjms.hacettepe.edu.tr/uploads/dd48204c-d9d6-4745-ad32-38e546c0c384.pdf. Acesso em: 07 dez. 20171549-3644https://repositorio.ufrn.br/handle/123456789/49657Journal of Mathematics and StatisticsExtreme value theoryGeneralized extreme value distributionGeneralized classes of distributionsEnvironmentalEconomic dataExtended generalized extreme value distribution with applications in environmental datainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleIn probability theory and statistics, the generalized extreme value (GEV) distribution is a family of continuous probability distributions developed within extreme value theory, which has wide applicability in several areas including hydrology, engineering, science, ecology and finance. In this paper, we propose three extensions of the GEV distribution that incorporate an additional parameter. These extensions are more flexible than the GEV distribution, i.e., the additional parameter introduces skewness and to vary tail weight. In these three cases, the GEV distribution is a particular case. The parameter estimation of these new distributions is done under the Bayesian paradigm, considering vague priors for the parameters. Simulation studies show the efficiency of the proposed models. Applications to river quotas and rainfall show that the generalizations can produce more efficient results than is the standard case with GEV distribution.info:eu-repo/semantics/openAccessengreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNORIGINALExtendedGeneralized_2015.pdfExtendedGeneralized_2015.pdfapplication/pdf448727https://repositorio.ufrn.br/bitstream/123456789/49657/1/ExtendedGeneralized_2015.pdf2bb0ff1da341c945cc8de80e751517e7MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.ufrn.br/bitstream/123456789/49657/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52123456789/496572022-10-31 18:14:05.763oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2022-10-31T21:14:05Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false
dc.title.pt_BR.fl_str_mv Extended generalized extreme value distribution with applications in environmental data
title Extended generalized extreme value distribution with applications in environmental data
spellingShingle Extended generalized extreme value distribution with applications in environmental data
Nascimento, Fernando
Extreme value theory
Generalized extreme value distribution
Generalized classes of distributions
Environmental
Economic data
title_short Extended generalized extreme value distribution with applications in environmental data
title_full Extended generalized extreme value distribution with applications in environmental data
title_fullStr Extended generalized extreme value distribution with applications in environmental data
title_full_unstemmed Extended generalized extreme value distribution with applications in environmental data
title_sort Extended generalized extreme value distribution with applications in environmental data
author Nascimento, Fernando
author_facet Nascimento, Fernando
Bourguignon, Marcelo
Leão, Jeremias
author_role author
author2 Bourguignon, Marcelo
Leão, Jeremias
author2_role author
author
dc.contributor.author.fl_str_mv Nascimento, Fernando
Bourguignon, Marcelo
Leão, Jeremias
dc.subject.por.fl_str_mv Extreme value theory
Generalized extreme value distribution
Generalized classes of distributions
Environmental
Economic data
topic Extreme value theory
Generalized extreme value distribution
Generalized classes of distributions
Environmental
Economic data
description In probability theory and statistics, the generalized extreme value (GEV) distribution is a family of continuous probability distributions developed within extreme value theory, which has wide applicability in several areas including hydrology, engineering, science, ecology and finance. In this paper, we propose three extensions of the GEV distribution that incorporate an additional parameter. These extensions are more flexible than the GEV distribution, i.e., the additional parameter introduces skewness and to vary tail weight. In these three cases, the GEV distribution is a particular case. The parameter estimation of these new distributions is done under the Bayesian paradigm, considering vague priors for the parameters. Simulation studies show the efficiency of the proposed models. Applications to river quotas and rainfall show that the generalizations can produce more efficient results than is the standard case with GEV distribution.
publishDate 2015
dc.date.issued.fl_str_mv 2015
dc.date.accessioned.fl_str_mv 2022-10-31T21:14:05Z
dc.date.available.fl_str_mv 2022-10-31T21:14:05Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.citation.fl_str_mv NASCIMENTO, F. F.; BOURGUIGNON, Marcelo; LEÃO, Jeremias. Extended generalized extreme value distribution with applications in environmental data. Hacettepe Journal of Mathematics and Statistics , v. 46, p. 1-1, 2015. Disponível em: http://www.hjms.hacettepe.edu.tr/uploads/dd48204c-d9d6-4745-ad32-38e546c0c384.pdf. Acesso em: 07 dez. 2017
dc.identifier.uri.fl_str_mv https://repositorio.ufrn.br/handle/123456789/49657
dc.identifier.issn.none.fl_str_mv 1549-3644
identifier_str_mv NASCIMENTO, F. F.; BOURGUIGNON, Marcelo; LEÃO, Jeremias. Extended generalized extreme value distribution with applications in environmental data. Hacettepe Journal of Mathematics and Statistics , v. 46, p. 1-1, 2015. Disponível em: http://www.hjms.hacettepe.edu.tr/uploads/dd48204c-d9d6-4745-ad32-38e546c0c384.pdf. Acesso em: 07 dez. 2017
1549-3644
url https://repositorio.ufrn.br/handle/123456789/49657
dc.language.iso.fl_str_mv eng
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dc.publisher.none.fl_str_mv Journal of Mathematics and Statistics
publisher.none.fl_str_mv Journal of Mathematics and Statistics
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