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<p>This paper considers the inference of unknown parameters for an extension of the new Pareto-type distribution based on progressive type-II censored data. First, the estimation of the model parameter using maximum likelihood and Bayesian methods has been discussed. The approximated confidence intervals and Bayesian credible intervals are discussed as well. We then establish a Bayesian optimal design with respect to variance minimization criteria. Monte Carlo simulations are implemented to compare different methods of estimation, and finally, two real data sets, where the first one represents the remission times (in months) of bladder cancer patients and the second one represents the repair times (in hours) for an airborne communication transceiver have been analyzed for illustrative purposes.</p>

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Bayesian analysis and optimal life testing for new Pareto distribution under progressive censoring

  • 期刊论文

  • 发表日期：

    2025年11月25日

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  • Prakash Chandra,
  • Yogesh Mani Tripathi,
  • Akbar Asgharzadeh

摘要

This paper considers the inference of unknown parameters for an extension of the new Pareto-type distribution based on progressive type-II censored data. First, the estimation of the model parameter using maximum likelihood and Bayesian methods has been discussed. The approximated confidence intervals and Bayesian credible intervals are discussed as well. We then establish a Bayesian optimal design with respect to variance minimization criteria. Monte Carlo simulations are implemented to compare different methods of estimation, and finally, two real data sets, where the first one represents the remission times (in months) of bladder cancer patients and the second one represents the repair times (in hours) for an airborne communication transceiver have been analyzed for illustrative purposes.