‎Expected Experimentation Time, Estimation and Prediction for the Power Lindley Distribution Based on Progressively Type II Censored Data ‎W‎ith Binomial Removals

Document Type : Original Paper

Authors

Department of Statistics, University of Mazandaran, Babolsar, Iran

Abstract

In this paper, the problem of estimation and prediction for the power Lindley distribution is studied based on progressively Type-II censored data with binomial removals. First, we work on the estimation of the parameters of the power Lindley distribution with the help of maximum likelihood and Bayesian methods. The Bayesian estimation is done based on the symmetric squared error loss function and the asymmetric general entropy loss function. Since the Bayesian estimates involve integrals that do not seem to have explicit forms, we use the Metropolis-Hastings algorithm to approximate these integrals. A simulation study is presented to evaluate the performance of the estimators of the parameters. In the sequel, the problem of one-sample and two-sample prediction is discussed. A real example is given to illustrate the application of the theoretical methods given in the paper. In addition, the problem of expected experimentation time is studied with the help of drawing plots. The paper ends with several conclusions.

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References

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Journal of Advanced Mathematical Modeling
Volume 11, Issue 2
June 2021
Pages 210-240

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History

  • Receive Date: 15 November 2019
  • Revise Date: 20 January 2021
  • Accept Date: 20 October 2020

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