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Research Article | Open Access

Estimation of P(X<Y) for Fréchet, reversed Weibull and Weibull distributions: Analytical expressions, simulations and applications

Tiago A. da Fonseca1Felipe S. Quintino2( )Luan C. S. M. Ozelim3Pushpa N. Rathie2
Gama Engineering College, University of Brasília, Brasília 72.444-240, Brazil
Department of Statistics, University of Brasília, Brasília 70.910-900, Brazil
Department of Civil and Environmental Engineering, University of Brasília, Brasília 70.910-900, Brazil
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Abstract

This 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 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.

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Networks and Heterogeneous Media
Pages 1424-1447

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Cite this article:
da Fonseca TA, Quintino FS, Ozelim LCSM, et al. Estimation of P(X<Y) for Fréchet, reversed Weibull and Weibull distributions: Analytical expressions, simulations and applications. Networks and Heterogeneous Media, 2024, 19(4): 1424-1447. https://doi.org/10.3934/nhm.2024061

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Received: 22 August 2024
Revised: 29 October 2024
Accepted: 04 November 2024
Published: 15 December 2024
©2024 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)