Extended generalized extreme value distribution with applications in environmental data
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , |
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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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 |
format |
article |
status_str |
publishedVersion |
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 |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Journal of Mathematics and Statistics |
publisher.none.fl_str_mv |
Journal of Mathematics and Statistics |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRN instname:Universidade Federal do Rio Grande do Norte (UFRN) instacron:UFRN |
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Universidade Federal do Rio Grande do Norte (UFRN) |
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UFRN |
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UFRN |
reponame_str |
Repositório Institucional da UFRN |
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Repositório Institucional da UFRN |
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