E-bayesian and H-bayesian Estimation of Inverse Power Lomax Distribution under Different Loss Functions with an Application of Clinical Data

In this paper, Expected Bayesian and Hierarchical Bayesian techniques have been discussed to estimate the shape parameter of the inverse power Lomax distribution. The proposed estimates for the shape parameter are obtained by using an informative gamma prior based on squared error, entropy and weighted balance loss functions. The definitions of the proposed estimators as well as their characteristics are provided. A Monte Carlo simulation is executed to compare the performance of the proposed estimators in terms of mean squared error. Finally a real life data set has been analyzed for further illustrations.