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Bayesian inference for exponentiated Pareto model with application to bladder cancer remission time

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  • Umesh Singh
  • Manoj Kumar
  • Sanjay Kumar Singh

Abstract

Maximum likelihood and Bayes estimators of the unknown parameters and the expected experiment times of the exponentiated Pareto model have been obtained for progressive type-II censored data with binomial removal scheme. Markov Chain Monte Carlo (MCMC) method is used to compute the Bayes estimates of the parameters of interest. The generalized entropy loss function and squared error loss function have been considered for obtaining the Bayes estimators. Comparisons are made between Bayesian and maximum likelihood (ML) estimators via Monte Carlo simulation. The proposed methodology is illustrated through real data.

Suggested Citation

  • Umesh Singh & Manoj Kumar & Sanjay Kumar Singh, 2014. "Bayesian inference for exponentiated Pareto model with application to bladder cancer remission time," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 15(3), pages 403-426, June.
    • Handle: RePEc:csb:stintr:v:15:y:2014:i:3:p:403-426
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    References listed on IDEAS

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    1. Shuo-Jye Wu & Chun-Tao Chang, 2003. "Inference in the Pareto distribution based on progressive Type II censoring with random removals," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(2), pages 163-172.
    2. Ng, H. K. T. & Chan, P. S. & Balakrishnan, N., 2002. "Estimation of parameters from progressively censored data using EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 39(4), pages 371-386, June.
    3. Arnold, Barry C. & Press, S. James, 1983. "Bayesian inference for pareto populations," Journal of Econometrics, Elsevier, vol. 21(3), pages 287-306, April.
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    Cited by:

    1. Ziyad A. Alhussain & Essam A. Ahmed, 2020. "Estimation of exponentiated Nadarajah-Haghighi distribution under progressively type-II censored sample with application to bladder cancer data," Indian Journal of Pure and Applied Mathematics, Springer, vol. 51(2), pages 631-657, June.

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