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Research Article | Open Access

Bivariate Chen distribution based on iterated FGM copula: Properties and applications

M. A. Alawady1,2Mohamed A. Abd Elgawad3( )Salem A. Alyami3H. M. Barakat1I. A. Husseiny1G. M. Mansour1T. S. Taher1M. O. Mohamed1,4
Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Department of Statistics and Operations Research, College of Science, Qassim University, Buraydah 51482, Saudi Arabia
Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Faculty of Computers and Information Systems, Egyptian Chinese University, Cairo 11528, Egypt
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Abstract

This paper introduced a bivariate model constructed by coupling the iterated Farlie-Gumbel-Morgenstern (IFGM) copula with Chen marginals, yielding the bivariate IFGM-Chen distribution (IFGM-CD). The model was particularly tailored for reliability and survival analysis, as it accommodated bathtub-shaped hazard rates and captured a broader spectrum of dependence structures than traditional FGM-based models. Fundamental statistical properties were investigated, including product moments, conditional distributions, and reliability functions. A dedicated section on the stress-strength model within the IFGM-CD framework was provided, offering new insights into component reliability under dependent stress and strength. For parameter estimation, both maximum likelihood (ML) and Bayesian approaches were employed, supplemented by asymptotic and bootstrap confidence intervals. Extensive Monte Carlo simulations validated the performance of the estimators, and two real-data applications demonstrated the model's practicality and flexibility.

CLC number: 60B12, 62G30

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AIMS Mathematics
Pages 5379-5408

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Cite this article:
Alawady MA, Abd Elgawad MA, Alyami SA, et al. Bivariate Chen distribution based on iterated FGM copula: Properties and applications. AIMS Mathematics, 2026, 11(3): 5379-5408. https://doi.org/10.3934/math.2026222

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Received: 28 December 2025
Revised: 12 February 2026
Accepted: 26 February 2026
Published: 15 March 2026
©2026 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)