Open AccessBayesian estimation of the shape parameter of generalized Rayleigh distribution under non-informative prior
Author(s) -
Yakubu Aliyu,
Abubakar Yahaya
Publication year - 2015
Publication title -
international journal of advanced statistics and probability
Language(s) - English
Resource type - Journals
ISSN - 2307-9045
DOI - 10.14419/ijasp.v4i1.5542
Subject(s) - rayleigh distribution , mathematics , prior probability , statistics , mean squared error , bayesian probability , bayes estimator , bayes' theorem , shape parameter , entropy (arrow of time) , monte carlo method , rayleigh scattering , distribution (mathematics) , probability density function , mathematical analysis , physics , quantum mechanics , optics
A decade ago, two-parameter Burr Type X distribution was introduced by Surles and Padgett [14] which was described as Generalized Rayleigh Distribution (GRD). This skewed distribution can be used quiet effectively in modelling life time data. In this work, Bayesian estimation of the shape parameter of GRD was considered under the assumption of non-informative prior. The estimates were obtained under the squared error, Entropy and Precautionary loss functions. Extensive Monte Carlo simulations were carried out to compare the performances of the Bayes estimates with that of MLEs. It was observed that the estimate under the Entropy loss function is more stable than the estimates under squared error loss function, Precautionary loss function and MLEs.