Bayesian Inference for Parameter and Reliability Function of Inverse Rayleigh Distribution Under Modified Squared Error Loss Function
Huda Abdullah, Raghda Aref · SSRN Electronic Journal · 2016
In this study, obtained some Bayes estimators based on Modified squared error loss function as well as Maximum likelihood estimator for scale parameter and reliability function of Inverse Rayleigh distribution. In order to get better understanding of our Bayesian analysis, we consider non-informative prior for the scale parameter using Jefferys prior information as well as informative prior density represented by Gamma distribution. Based on Monte-Carlo simulation study, the behavior of Bayes estimates of the scale parameter of inverse Rayleigh distribution have been compared depending on the mean squared errors (MSE’s), while the estimates of the reliability function have been compared depending on the Integrated mean squared errors (IMSE’s). In the current study, we observed that, the performance of Bayes estimator for the scale parameter and reliability function under Modified squared error loss function with Gamma prior is better than the corresponding estimators with Jefferys prior, for all cases.