Estimation and Prediction of Inverse Lomax Model via Bayesian Approach

Statistical analysis via Bayesian approach is a common practice applied to draw inferences about unknown parameter(s) of a probability distribution. This article includes Bayesian inference of unknown shape parameter of two-parameter inverse Lomax distribution. Bayes estimators and Bayes posterior risks for the same parameter are derived under various symmetric and asymmetric loss functions along with the use of different informative and uninformative priors. Bayesian interval estimation and hypotheses testing is also carried out for further analysis. A comparison between Bayes estimates and ML estimate is made after getting the simulated and real-data results. Further analysis consists of making predictions about an observation associated with a probability distribution using square log loss function.