http://repositorio.unb.br/handle/10482/50621
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ARTIGO_EstimationPXYStress.pdf | 451,98 kB | Adobe PDF | Voir/Ouvrir |
Titre: | Estimation of P(X < Y) stress-strength reliability measures for a class of asymmetric distributions : the case of three-parameter p-max stable laws |
Auteur(s): | Quintino, Felipe Sousa Rathie, Pushpa Narayan Ozelim, Luan Carlos de Sena Monteiro Fonseca, Tiago Alves da |
metadata.dc.identifier.orcid: | https://orcid.org/0000-0003-0286-0541 https://orcid.org/0000-0002-9790-369X https://orcid.org/0000-0002-2581-0486 https://orcid.org/0009-0004-5147-4393 |
metadata.dc.contributor.affiliation: | University of Brasilia, Department of Statistics University of Brasilia, Department of Statistics University of Brasilia, Department of Civil and Environmental Engineering University of Brasilia, Gama Engineering College |
Assunto:: | Confiabilidade tensão-resistência Distribuições assimétricas Probabilidades |
Date de publication: | 2024 |
Editeur: | MDPI |
Référence bibliographique: | QUINTINO, 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. |
Abstract: | Asymmetric 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. |
metadata.dc.description.unidade: | Instituto de Ciências Exatas (IE) Departamento de Estatística (IE EST) Faculdade de Tecnologia (FT) Departamento de Engenharia Civil e Ambiental (FT ENC) Faculdade UnB Gama (FGA) |
Licença:: | Copyright: © 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/). |
DOI: | https:/doi.org/10.3390/sym16070837 |
Collection(s) : | Artigos publicados em periódicos e afins |
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