On estimation of two-component mixture inverse Lomax model via Bayesian approach

Abstract

A mixture distribution is the probability distribution of observations in the pooled population which is used to make statistical inferences about the characteristics of the sub-populations on the basis of sample data from the joint population. This article comprises such sort of study for unknown parameters of two-component mixture inverse Lomax distribution based on Bayesian thoughts. Bayes estimators and Bayes posterior risks for the parameters are derived under various loss functions along with the use of conjugate priors. Numerical results for Bayes estimates and Bayes risks are obtained by simulation as well as real data. The study also includes Maximum likelihood estimation for the comparisons with Bayesian estimation.