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This paper develops Bayesian estimation and prediction, for a mixture of Weibull and Lomax distributions, in the context of the new life test plan called progressive first failure censored samples. Maximum likelihood  estimation and Bayes estimation, under informative and non-informative priors, are obtained using Markov Chain Monte Carlo methods, based on the symmetric square error Loss function and the asymmetric linear exponential (LINEX) and general entropy loss functions. The maximum likelihood estimates and the different Bayes estimates are compared via a Monte Carlo simulation study. Finally, Bayesian prediction intervals for future observations are obtained using a numerical example


Mixture model Progressive First Failure Censored Scheme Loss Function Maximum Likelihood Estimation Bayesian Estimation and Prediction Markov Chain Monte Carlo.

Article Details

Author Biographies

Mohamed M. Mahmoud, Ain Shams University

Department of Mathematics, Professor

Manal Mohamed Nassar, Ain Shams University, Cairo, Egypt

Department of Mathematics, Professor

Marwa Ahmed Aefa, Ain Shams University

Department of Mathematics, Ph. D. Student

How to Cite
Mahmoud, M. M., Nassar, M. M., & Aefa, M. A. (2020). Bayesian Estimation and Prediction Based on Progressively First Failure Censored Scheme from a Mixture of Weibull and Lomax Distributions. Pakistan Journal of Statistics and Operation Research, 16(2), 357-372.


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