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<p>This work discusses the estimate of stress-strength (SS) reliability for the xgamma distribution using progressively censored samples, a common scenario in the field of reliability engineering. The xgamma distribution is chosen for its adaptability in modelling various forms of lifetime data, while progressive censoring effectively reflects real-world scenarios where complete data is often unavailable. The classical and Bayesian estimators for assessing the reliability of SS have been constructed. Classical estimation employs maximum likelihood for point estimation and asymptotic confidence intervals for interval estimation techniques. In Bayesian estimation, the MCMC method is used to obtain the Bayesian point and interval estimates of SS reliability using the squared error loss (SEL) function and gamma priors. A Monte Carlo simulation is conducted to analyse the results of the effort. A real data set is evaluated for explanatory purposes.</p>

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Stress-strength reliability estimation from xgamma distribution based on progressively censored samples

  • 期刊论文

  • 发表日期：

    2025年5月12日

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  • Anju Grewal,
  • Ranjan Kumar Sahoo,
  • Kapil Kumar,
  • Indrajeet Kumar

摘要

This work discusses the estimate of stress-strength (SS) reliability for the xgamma distribution using progressively censored samples, a common scenario in the field of reliability engineering. The xgamma distribution is chosen for its adaptability in modelling various forms of lifetime data, while progressive censoring effectively reflects real-world scenarios where complete data is often unavailable. The classical and Bayesian estimators for assessing the reliability of SS have been constructed. Classical estimation employs maximum likelihood for point estimation and asymptotic confidence intervals for interval estimation techniques. In Bayesian estimation, the MCMC method is used to obtain the Bayesian point and interval estimates of SS reliability using the squared error loss (SEL) function and gamma priors. A Monte Carlo simulation is conducted to analyse the results of the effort. A real data set is evaluated for explanatory purposes.