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<p>This study investigates the reliability analysis of a multicomponent system in which stress and strength are modeled as dependent random variables, using progressively Type-II censored data. Both variables are assumed to follow Kumaraswamy distributions with different shape parameters, and their dependency structure is characterized via a Clayton copula. Maximum likelihood estimators (MLE) and bootstrap confidence intervals are derived for the unknown parameters and system reliability. Furthermore, Bayesian point estimates and highest posterior density (HPD) credible intervals are obtained under various loss functions. The performance of the proposed methods is evaluated through Monte Carlo simulations, and a real data analysis is presented to demonstrate the applicability of our study.</p>

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Reliability Analysis of Stress-Strength Model for Multicomponent Systems Using Copula Under Progressive Censoring

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • J. Cai,
  • Y. Yang,
  • L. Zhong,
  • R. Wang

摘要

This study investigates the reliability analysis of a multicomponent system in which stress and strength are modeled as dependent random variables, using progressively Type-II censored data. Both variables are assumed to follow Kumaraswamy distributions with different shape parameters, and their dependency structure is characterized via a Clayton copula. Maximum likelihood estimators (MLE) and bootstrap confidence intervals are derived for the unknown parameters and system reliability. Furthermore, Bayesian point estimates and highest posterior density (HPD) credible intervals are obtained under various loss functions. The performance of the proposed methods is evaluated through Monte Carlo simulations, and a real data analysis is presented to demonstrate the applicability of our study.