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

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  • Prakash Chandra

    (SRM University AP, Department of Mathematics)

  • Yogesh Mani Tripathi

    (Indian Institute of Technology Patna, Department of Mathematics)

  • Akbar Asgharzadeh

    (University of Mazandaran, Department of Statistics)

Abstract

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.

Suggested Citation

  • Prakash Chandra & Yogesh Mani Tripathi & Akbar Asgharzadeh, 2025. "Bayesian analysis and optimal life testing for new Pareto distribution under progressive censoring," METRON, Springer;Sapienza Università di Roma, vol. 83(3), pages 365-393, December.
    • Handle: RePEc:spr:metron:v:83:y:2025:i:3:d:10.1007_s40300-025-00299-6
    DOI: 10.1007/s40300-025-00299-6
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