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dc.contributor.authorQuintino, Felipe Sousa-
dc.contributor.authorRathie, Pushpa Narayan-
dc.contributor.authorOzelim, Luan Carlos de Sena Monteiro-
dc.contributor.authorFonseca, Tiago Alves da-
dc.date.accessioned2024-10-20T17:28:26Z-
dc.date.available2024-10-20T17:28:26Z-
dc.date.issued2024-
dc.identifier.citationQUINTINO, Felipe Sousa et al. Estimation of P(X < Y) stress-strength reliability measures for a class of asymmetric distributions: the case of three-parameter p-max stable laws. Simetria, [S. l.], v. 16, n. 7, 837, 2024. DOI: https:/doi.org/10.3390/sym16070837. Disponível em: https://www.mdpi.com/2073-8994/16/7/837.pt_BR
dc.identifier.urihttp://repositorio.unb.br/handle/10482/50621-
dc.language.isoengpt_BR
dc.publisherMDPIpt_BR
dc.rightsAcesso Abertopt_BR
dc.titleEstimation of P(X < Y) stress-strength reliability measures for a class of asymmetric distributions : the case of three-parameter p-max stable lawspt_BR
dc.typeArtigopt_BR
dc.subject.keywordConfiabilidade tensão-resistênciapt_BR
dc.subject.keywordDistribuições assimétricaspt_BR
dc.subject.keywordProbabilidadespt_BR
dc.rights.licenseCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).pt_BR
dc.identifier.doihttps:/doi.org/10.3390/sym16070837pt_BR
dc.description.abstract1Asymmetric distributions are frequently seen in real-world datasets due to a number of factors, such as sample biases and nonlinear interactions between the variables observed. Thus, in order to better characterize real-world phenomena, studying asymmetric distribution is of great interest. In this work, we derive stress–strength reliability formulas of the type P(X < Y) when both X and Y follow p-max stable laws with three parameters, which are inherently asymmetric. The new relations are given in terms of extreme-value H-functions and have been obtained under fewer parameter restrictions when compared to similar results in the literature. We estimate the parameters of the p-max stable laws by a stochastic optimization method and the stress–strength probability by a maximum likelihood procedure. The performance of the analytical models is evaluated through simulations and real-life dataset modeling.pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-0286-0541pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-9790-369Xpt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-2581-0486pt_BR
dc.identifier.orcidhttps://orcid.org/0009-0004-5147-4393pt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Statisticspt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Statisticspt_BR
dc.contributor.affiliationUniversity of Brasilia, Department of Civil and Environmental Engineeringpt_BR
dc.contributor.affiliationUniversity of Brasilia, Gama Engineering Collegept_BR
dc.description.unidadeInstituto de Ciências Exatas (IE)pt_BR
dc.description.unidadeDepartamento de Estatística (IE EST)pt_BR
dc.description.unidadeFaculdade de Tecnologia (FT)pt_BR
dc.description.unidadeDepartamento de Engenharia Civil e Ambiental (FT ENC)pt_BR
dc.description.unidadeFaculdade UnB Gama (FGA)pt_BR
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