• There are currently no refbacks.

Copyright (c) 2017 Pakistan Journal of Statistics and Operation Research

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


Bayesian Analysis of the Mixture of Frechet Distribution under Different Loss Functions


Bayes Estimators, Censoring, Informative prior, Loss Functions, Posterior Risks


This paper has to do with 3-component mixture of the Frechet dis- tributions when the shape parameter is known under Bayesian view point. The type-I right censored sampling scheme is considered due to its extensive use in reliability theory and survival analysis. Taking dif- ferent non-informative and informative priors, Bayes estimates of the parameter of the mixture model along with their posterior risks are derived under squared error loss function, precautionary loss function and DeGroot loss function. In case, no or little prior information is available, elicitation of hyper parameters is given. In order to study numerically, the execution of the Bayes estimators under different loss functions, their statistical properties have been simulated for different sample sizes and test termination times. A real life data example is also given to illustrate the study.





Pakistan Journal of Statistics and Operation Research; Vol. 13 No. 3, 2017

Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810