Bayes Inference for the Weibull-Geometric Distribution based on Progressive Hybrid Censored Data

Authors

Mohamed Mousa, Mathematics Department, Faculty of Science, Assiut University, Assiut, EgyptFollow
Zeinhum Jaheen, Mathematics Department, Faculty of Science, Assiut University, Assiut, EgyptFollow
Sara Ali, Mathematics Department, Faculty of Science, Assiut University, Assiut, EgyptFollow

Abstract

The challenge of estimating the parameters of the Weibull-Geometric distribution using progressively type-I hybrid censored data is addressed in this study. For this, the maximum likelihood and Bayes methods of estimation are applied. The Bayes estimates are calculated using the Markov Chain Monte Carlo (MCMC) method. Through a Monte Carlo simulation investigation, the Bayes estimates of the parameters under two alternative loss functions are investigated and compared to their corresponding maximum likelihood estimates. For illustration, a practical set of data is used.

Recommended Citation

Mousa, Mohamed; Jaheen, Zeinhum; and Ali, Sara (2022) "Bayes Inference for the Weibull-Geometric Distribution based on Progressive Hybrid Censored Data," Information Sciences Letters: Vol. 11 : Iss. 5 , PP -.
Available at: https://digitalcommons.aaru.edu.jo/isl/vol11/iss5/4