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<p>This study explores the challenge of estimating the reliability of multicomponent stress-strength (MSS) systems under progressively censored data. Specifically, we delve into scenarios where both stress and strength variables adhere to Weighted Exponential-Lindley lifetime models with distinct parameters. Various estimation methods, including maximum likelihood, asymptotic confidence intervals, Bayesian analysis, and highest posterior density (HPD) credible intervals, are applied to gauge MSS reliability. The Bayes estimate of MSS reliability is determined using the Metropolis–Hastings algorithm, incorporating a squared error loss function (SELF) and linear exponential (LINEX) loss function. To assess the efficacy of the proposed estimates, a comprehensive simulation study is conducted. Furthermore, the practical application of these methods is demonstrated through the analysis of a real-life example.</p>

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Multicomponent Stress-Strength Reliability Estimation of Weighted Exponential-Lindley Lifetime Model Under Progressive Censoring

  • 期刊论文

  • 发表日期：

    2025年5月29日

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  • Sunita Sharma,
  • Vinod Kumar

摘要

This study explores the challenge of estimating the reliability of multicomponent stress-strength (MSS) systems under progressively censored data. Specifically, we delve into scenarios where both stress and strength variables adhere to Weighted Exponential-Lindley lifetime models with distinct parameters. Various estimation methods, including maximum likelihood, asymptotic confidence intervals, Bayesian analysis, and highest posterior density (HPD) credible intervals, are applied to gauge MSS reliability. The Bayes estimate of MSS reliability is determined using the Metropolis–Hastings algorithm, incorporating a squared error loss function (SELF) and linear exponential (LINEX) loss function. To assess the efficacy of the proposed estimates, a comprehensive simulation study is conducted. Furthermore, the practical application of these methods is demonstrated through the analysis of a real-life example.