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Bayesian Estimation for GDUS Exponential Distribution Under Type-I Progressive Hybrid Censoring

Author

Listed:
  • Teena Goyal

    (Banasthali Vidyapith)

  • Piyush K. Rai

    (Banaras Hindu University)

  • Sandeep K. Maurya

    (Central University of South Bihar)

Abstract

This paper describes the classical and Bayesian inferences for the generalized DUS exponential distribution under type-I progressive hybrid censored data. In classical estimation; maximum likelihood estimator is used for obtaining estimates of the parameters. While in Bayesian context; two different losses namely squared error and linex loss function are used for estimation purpose. Metropolis–Hasting algorithm has applied to generate Markov chain Monte Carlo samples from the posterior density. In case of interval estimation; asymptotic confidence intervals and highest posterior density intervals for the unknown parameters are computed. The performance of estimators for different value of the parameters have done on the basis of mean square errors and risks. Lastly, a dataset is used to illustrate the proposed censoring methodology in a real-world situation.

Suggested Citation

  • Teena Goyal & Piyush K. Rai & Sandeep K. Maurya, 2020. "Bayesian Estimation for GDUS Exponential Distribution Under Type-I Progressive Hybrid Censoring," Annals of Data Science, Springer, vol. 7(2), pages 307-345, June.
    • Handle: RePEc:spr:aodasc:v:7:y:2020:i:2:d:10.1007_s40745-020-00263-3
    DOI: 10.1007/s40745-020-00263-3
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    References listed on IDEAS

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    Cited by:

    1. Varun Agiwal, 2023. "Bayesian Estimation of Stress Strength Reliability from Inverse Chen Distribution with Application on Failure Time Data," Annals of Data Science, Springer, vol. 10(2), pages 317-347, April.
    2. Sandeep Kumar Maurya & Saralees Nadarajah, 2021. "Poisson Generated Family of Distributions: A Review," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 484-540, November.

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