Title:	Estimation of P(X < Y) stress-strength reliability measures for a class of asymmetric distributions : the case of three-parameter p-max stable laws
Authors:	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
Issue Date:	2024
Publisher:	MDPI
Citation:	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
Appears in Collections:	Artigos publicados em periódicos e afins

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.