The Bayesian Estimation and Prediction Process Applied to a Mixture of Weibull and Gompertz Distributions Based on Type-I censoring
El-Din, M., Sadek, A., AL-Dugin, A. (2645). The Bayesian Estimation and Prediction Process Applied to a Mixture of Weibull and Gompertz Distributions Based on Type-I censoring. EKB Journal Management System, 2024(1), -. doi: 10.58675/2636-3305.1685
M. M. Mohie El-Din; A. Sadek; A. AL-Dugin. "The Bayesian Estimation and Prediction Process Applied to a Mixture of Weibull and Gompertz Distributions Based on Type-I censoring". EKB Journal Management System, 2024, 1, 2645, -. doi: 10.58675/2636-3305.1685
El-Din, M., Sadek, A., AL-Dugin, A. (2645). 'The Bayesian Estimation and Prediction Process Applied to a Mixture of Weibull and Gompertz Distributions Based on Type-I censoring', EKB Journal Management System, 2024(1), pp. -. doi: 10.58675/2636-3305.1685
El-Din, M., Sadek, A., AL-Dugin, A. The Bayesian Estimation and Prediction Process Applied to a Mixture of Weibull and Gompertz Distributions Based on Type-I censoring. EKB Journal Management System, 2645; 2024(1): -. doi: 10.58675/2636-3305.1685

The Bayesian Estimation and Prediction Process Applied to a Mixture of Weibull and Gompertz Distributions Based on Type-I censoring

Al-Azhar Bulletin of Science
Volume 2024, Issue 1, January 2024
DOI: 10.58675/2636-3305.1685
Authors
M. M. Mohie El-Din; A. Sadek; A. AL-Dugin
Dept. of Math., Faculty of Science, Al-Azhar University, Nasr City, Cairo, Egypt
Abstract
We examine different methods to estimate the parameters of a lifetime model represented by a mixture of Weibull and Gompertz distributions, based on Type-I censoring. We derive Bayes estimators with a variety of loss functions, including symmetric Squared Error, asymmetric Linear Exponential, and General Entropy, utilizing both informative and noninformative priors. We also go over how to create the model's two-sample Bayesian prediction intervals. To demonstrate these methods, we provide computational results through Monte Carlo simulations and real data.
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