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<p>In this study, we examine the estimation of reliability performance metrics when life testing is conducted across multiple test facilities under block progressive Type-II censoring. Accounting for variations among different test environments, reliability estimates are derived assuming that product lifetimes follow a Lomax distribution. Both classical and hierarchical Bayesian frameworks are employed to obtain these estimates. Additionally, the existence and uniqueness of maximum likelihood estimators for the model parameters are demonstrated, and approximate confidence intervals are constructed using asymptotic theory and the delta method. Furthermore, Bayesian estimates are computed within a hierarchical framework utilizing a hybrid Metropolis–Hastings sampling technique. Finally, the effectiveness of the proposed methods is evaluated through an extensive simulation study, and their practical applicability is demonstrated using a real-world data example.</p>

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Reliability Estimation for Lomax Distribution based on Block Progressive Type-II Censoring Methodology

  • 期刊论文

  • 发表日期：

    2026年2月10日

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  • K P Aswathi,
  • M Kumar

摘要

In this study, we examine the estimation of reliability performance metrics when life testing is conducted across multiple test facilities under block progressive Type-II censoring. Accounting for variations among different test environments, reliability estimates are derived assuming that product lifetimes follow a Lomax distribution. Both classical and hierarchical Bayesian frameworks are employed to obtain these estimates. Additionally, the existence and uniqueness of maximum likelihood estimators for the model parameters are demonstrated, and approximate confidence intervals are constructed using asymptotic theory and the delta method. Furthermore, Bayesian estimates are computed within a hierarchical framework utilizing a hybrid Metropolis–Hastings sampling technique. Finally, the effectiveness of the proposed methods is evaluated through an extensive simulation study, and their practical applicability is demonstrated using a real-world data example.