Please use this identifier to cite or link to this item: https://repositorio.ufrn.br/handle/123456789/49657
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dc.contributor.authorNascimento, Fernando-
dc.contributor.authorBourguignon, Marcelo-
dc.contributor.authorLeão, Jeremias-
dc.date.accessioned2022-10-31T21:14:05Z-
dc.date.available2022-10-31T21:14:05Z-
dc.date.issued2015-
dc.identifier.citationNASCIMENTO, 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. 2017pt_BR
dc.identifier.issn1549-3644-
dc.identifier.urihttps://repositorio.ufrn.br/handle/123456789/49657-
dc.languageenpt_BR
dc.publisherJournal of Mathematics and Statisticspt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectExtreme value theorypt_BR
dc.subjectGeneralized extreme value distributionpt_BR
dc.subjectGeneralized classes of distributionspt_BR
dc.subjectEnvironmentalpt_BR
dc.subjectEconomic datapt_BR
dc.titleExtended generalized extreme value distribution with applications in environmental datapt_BR
dc.typearticlept_BR
dc.description.resumoIn 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.pt_BR
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