Bayesian Modeling of 3-Component Mixture of Exponentiated Inverted Weibull Distribution under Noninformative Prior

Abstract EN

Bayesian study of 3-component mixture modeling of exponentiated inverted Weibull distribution under right type I censoring technique is conducted in this research work.

The posterior distribution of the parameters is obtained assuming the noninformative (Jeffreys and uniform) priors.

The different loss functions (squared error, quadratic, precautionary, and DeGroot loss function) are used to obtain the Bayes estimators and posterior risks.

The performance of the Bayes estimators through posterior risks under the said loss functions is investigated through simulation process.

Real data analysis of tensile strength of carbon fiber is also applied for 3 components to conclude the presentation of Bayes estimators.

The limiting expressions are also elaborated for Bayes estimators and posterior risks in this study.

The impact of some test termination times and sample sizes is reported on Bayes estimators.