Bayesian Estimation of the Parameter of Rayleigh Distribution under the Extended Jeffrey’s Prior


In this paper, Bayes estimators of Rayleigh parameter and its associated risk based on extended Jeffrey's prior under the  assumptions of both symmetric loss function (squared error loss) and asymmetric (precautionary and general entropy ) loss function have been derived. We also derive the highest posterior density (HPD) and equal-tail prediction intervals for the parameter as well as the HPD prediction intervals for future observation.
Monte Carlo simulations are performed to compare the performances of the Bayes estimates under different situations. Finally, An illustrative example is presented to assess how the Rayleigh  distribution fits a real data set.

DOI Code: 10.1285/i20705948v5n1p44

Keywords: Bayes estimator; Predictive distribution; Predictive intervals; Prior distribution

Full Text: PDF

Creative Commons License
This work is licensed under a Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia License.