Bayesian Estimation and Prediction for a Mixture of Weibull and Lomax Distributions

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This paper focuses on the study of a mixture of two components Weibull and Lomax distributions 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 loss function (SELF) and the asymmetric linear exponential (LINEX) and general entropy (GELF) loss functions. Bayesian prediction has been considered for future observation based on the observed sample. Finally, a simulation study as well as a numerical example are carried out to compare the performance of the estimators obtained.