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Statistical Inference for the Modified Fréchet-Lomax Exponential Distribution Under Constant-Stress PALT with Progressive First-Failure Censoring

Author

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  • Ahmed T. Farhat

    (Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt)

  • Dina A. Ramadan

    (Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt)

  • Hanan Haj Ahmad

    (Department of Basic Science, The General Administration of Preparatory Year, King Faisal University, Hofuf 31982, Al-Ahsa, Saudi Arabia
    Department of Mathematics and Statistics, College of Science, King Faisal University, Hofuf 31982, Al-Ahsa, Saudi Arabia)

  • Beih S. El-Desouky

    (Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt)

Abstract

Life testing of products often requires extended observation periods. To shorten the duration of these tests, products can be subjected to more extreme conditions than those encountered in normal use; an approach known as accelerated life testing (ALT) is considered. This study investigates the estimation of unknown parameters and the acceleration factor for the modified Fréchet-Lomax exponential distribution (MFLED), utilizing Type II progressively first-failure censored (PFFC) samples obtained under the framework of constant-stress partially accelerated life testing (CSPALT). Maximum likelihood (ML) estimation is employed to obtain point estimates for the model parameters and the acceleration factor, while the Fisher information matrix is used to construct asymptotic confidence intervals (ACIs) for these estimates. To improve the precision of inference, two parametric bootstrap methods are also implemented. In the Bayesian context, a method for eliciting prior hyperparameters is proposed, and Bayesian estimates are obtained using the Markov Chain Monte Carlo (MCMC) method. These estimates are evaluated under both symmetric and asymmetric loss functions, and the corresponding credible intervals (CRIs) are computed. A comprehensive simulation study is conducted to compare the performance of ML, bootstrap, and Bayesian estimators in terms of mean squared error and coverage probabilities of confidence intervals. Finally, real-world failure time data of light-emitting diodes (LEDs) are analyzed to demonstrate the applicability and efficiency of the proposed methods in practical reliability studies, highlighting their value in modeling the lifetime behavior of electronic components.

Suggested Citation

  • Ahmed T. Farhat & Dina A. Ramadan & Hanan Haj Ahmad & Beih S. El-Desouky, 2025. "Statistical Inference for the Modified Fréchet-Lomax Exponential Distribution Under Constant-Stress PALT with Progressive First-Failure Censoring," Mathematics, MDPI, vol. 13(16), pages 1-28, August.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:16:p:2585-:d:1723118
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    References listed on IDEAS

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    1. Amal S. Hassan & Said G. Nassr & Sukanta Pramanik & Sudhansu S. Maiti, 2020. "Estimation in Constant Stress Partially Accelerated Life Tests for Weibull Distribution Based on Censored Competing Risks Data," Annals of Data Science, Springer, vol. 7(1), pages 45-62, March.
    2. Wu, Shuo-Jye & Kus, Coskun, 2009. "On estimation based on progressive first-failure-censored sampling," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3659-3670, August.
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