Open AccessBayesian Estimation for Two Parameters of Gamma Distribution Under Precautionary Loss Function
Author(s) -
Loaiy F. Naji,
Huda A. Rasheed
Publication year - 2019
Publication title -
mağallaẗ ibn al-haytam li-l-ʻulūm al-ṣirfaẗ wa-al-taṭbīqiyyaẗ/ibn al-haitham journal for pure and applied sciences
Language(s) - English
Resource type - Journals
eISSN - 2521-3407
pISSN - 1609-4042
DOI - 10.30526/32.1.1914
Subject(s) - prior probability , estimator , bayes estimator , gamma distribution , mathematics , mean squared error , bayes' theorem , statistics , bayesian probability , inverse gamma distribution , exponential function , scale parameter , exponential distribution , moment (physics) , physics , asymptotic distribution , mathematical analysis , normal gamma distribution , classical mechanics
In the current study, the researchers have been obtained Bayes estimators for the shape and scale parameters of Gamma distribution under the precautionary loss function, assuming the priors, represented by Gamma and Exponential priors for the shape and scale parameters respectively. Moment, Maximum likelihood estimators and Lindley’s approximation have been used effectively in Bayesian estimation.
Based on Monte Carlo simulation method, those estimators are compared depending on the mean squared errors (MSE’s). The results show that, the performance of Bayes estimator under precautionary loss function with Gamma and Exponential priors is better than other estimates in all cases.