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

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.