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dc.contributor.authorFonseca, Tiago Alves dapt_BR
dc.contributor.authorQuintino, Felipe Sousapt_BR
dc.contributor.authorOzelim, Luan Carlos de Sena Monteiropt_BR
dc.contributor.authorRathie, Pushpa Narayanpt_BR
dc.date.accessioned2026-07-28T12:00:38Z-
dc.date.available2026-07-28T12:00:38Z-
dc.date.issued2024pt_BR
dc.identifier.citationFONSECA, Tiago A. da et al. Estimation of P(X < Y) for Fréchet, reversed Weibull and Weibull distributions: analytical expressions, simulations and applications. Networks and Heterogeneous Media, v. 19, n. 4, p. 1424-1447, 2024. DOI: https://doi.org/10.3934/nhm.2024061. Disponível em: https://doi.org/10.3934/nhm.2024061pt_BR
dc.identifier.urihttps://doi.org/10.3934/nhm.2024061pt_BR
dc.identifier.urihttp://repositorio.unb.br/handle/10482/55507-
dc.language.isoeng-
dc.publisherAmerican Institute of Mathematical Sciences (AIMS)pt_BR
dc.rightsAcesso Abertopt_BR
dc.titleEstimation of P(X < Y) for Fréchet, reversed Weibull and Weibull distributions : analytical expressions, simulations and applicationspt_BR
dc.typeArtigopt_BR
dc.subject.keywordDistribuição (Probabilidades)pt_BR
dc.subject.keywordTeoria dos valores extremospt_BR
dc.subject.keywordConfiabilidade stress-strengthpt_BR
dc.subject.keywordSimulação de Monte Carlopt_BR
dc.subject.keywordDistribuição de Fréchetpt_BR
dc.subject.keywordDistribuição de Weibullpt_BR
dc.subject.keywordModelagem estatísticapt_BR
dc.rights.licenseThis is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)-
dc.identifier.doihttps://doi.org/10.3934/nhm.2024061pt_BR
dc.description.abstract1This work aimed to derive new analytical formulas for the stress–strength reliability of the type $ P(X < Y) $ when both $ X $ and $ Y $ follow Fréchet, reversed Weibull or Weibull distributions. The new expressions were given in terms of extreme value $ \mathbb{H} $-functions and have been obtained under fewer parameter restrictions while compared to similar results in the literature of these distributions. The performance of the maximum likelihood estimator was evaluated through Monte-Carlo simulations and the results were compared with a nonparametric estimator. Three real dataset applications were carried out. First, we analyzed the statistical behavior of financial assets' returns, showing how $ P(X < Y) $ can be used to build an interesting approach to perform asset selection. Second, minimum monthly flows of water were analyzed. Finally, we compared failure voltage levels of two types of electrical cable insulation. For all the real case applications, confidence intervals for $ P(X < Y) $ were obtained by Bootstrap methods.pt_BR
dc.contributor.affiliationUniversity of Brasília, Gama Engineering Collegept_BR
dc.contributor.affiliationUniversity of Brasília, Department of Statisticspt_BR
dc.contributor.affiliationUniversity of Brasília, Department of Civil and Environmental Engineeringpt_BR
dc.contributor.affiliationUniversity of Brasília, Department of Statisticspt_BR
dc.description.unidadeFaculdade de Ciências e Tecnologias em Engenharia (FCTE) – Campus UnB Gama-
dc.description.unidadeInstituto de Ciências Exatas (IE)-
dc.description.unidadeDepartamento de Estatística (IE EST)-
dc.description.unidadeFaculdade de Tecnologia (FT)-
dc.description.unidadeDepartamento de Engenharia Civil e Ambiental (FT ENC)-
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