Bayesian Analysis of Inverse Rayleigh Distribution under Non-Informative Prior for Different Loss Functions

Abstract

In this paper, we tend to acquire Bayes estimators for the unknown parameter of an inverse Rayleigh distribution (IRD). Bayes estimators are obtained beneath symmetric squared error loss function (SELF) and asymmetric loss functions by employing a non-informative prior. The performance of the estimators is assessed on the idea of their relative risk under the different loss functions. We also obtained the risk functions and risk efficiencies associated with the different Bayes estimators under the different loss functions and compared the performance of these estimators through simulation study. Finally, a numerical study is provided from which we concluded that minimum expected loss function is better than SELF, De-Groot loss function and precautionary loss function.