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<p><?tk 4?>This paper develops statistical inference procedures for the parameters of two Xgamma populations under a joint progressive Type-II censoring (JPT-IIC) scheme. Maximum likelihood estimation along with asymptotic confidence intervals and Bayesian estimation using the Tierney–Kadane approximation and the Metropolis–Hastings algorithm under the Linex loss function are considered. Both non-informative and informative prior distributions are employed to obtain Bayesian estimates. A Monte Carlo simulation study is conducted to compare the performance of the proposed estimators, indicating that Bayesian methods with informative priors generally provide improved estimation accuracy and shorter credible intervals. The proposed methodology is illustrated using two real data sets involving air-conditioning system failure times and bank customer waiting times. The results demonstrate that the Xgamma distribution provides an adequate and flexible fit to the data and highlight the practical applicability of the proposed methods in reliability and lifetime data analysis.</p>

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Inference for two xgamma populations based on joint progressive type-II censored samples

  • 期刊论文

  • 发表日期：

    2026年3月10日

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  • Anju Grewal,
  • Ranjan Kumar Sahoo,
  • Kapil Kumar,
  • Anita Kumari

摘要

This paper develops statistical inference procedures for the parameters of two Xgamma populations under a joint progressive Type-II censoring (JPT-IIC) scheme. Maximum likelihood estimation along with asymptotic confidence intervals and Bayesian estimation using the Tierney–Kadane approximation and the Metropolis–Hastings algorithm under the Linex loss function are considered. Both non-informative and informative prior distributions are employed to obtain Bayesian estimates. A Monte Carlo simulation study is conducted to compare the performance of the proposed estimators, indicating that Bayesian methods with informative priors generally provide improved estimation accuracy and shorter credible intervals. The proposed methodology is illustrated using two real data sets involving air-conditioning system failure times and bank customer waiting times. The results demonstrate that the Xgamma distribution provides an adequate and flexible fit to the data and highlight the practical applicability of the proposed methods in reliability and lifetime data analysis.