Bayesian Estimation and Prediction from a Mixture of Weibull and Gompertz Distributions

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Abdulqader Al-Dugin
Mostafa Mohie El-Din
Amr Sadek

Abstract

We study different methods for estimation the parameters of a mixture of Weibull and Gompertz distributions as a lifetime model, based on a complete sample. Maximum likelihood estimation and Bayes estimation under informative and non-informative priors have been obtained using the symmetric squared error (SE) loss function, the asymmetric Linear exponential (LINEX) loss function and general entropy (GE) loss function. Also, we discuss two-sample Bayesian prediction intervals of the proposed model. For the illustration of the developing results, some computation results for the proposed model is presented.

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How to Cite
Al-Dugin, A., Mohie El-Din, M., & Sadek, A. (2023). Bayesian Estimation and Prediction from a Mixture of Weibull and Gompertz Distributions. Advances in Systems Science and Applications, 23(04), 60-75. https://doi.org/10.25728/assa.2023.23.04.1428
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