Open AccessBayesian and Non-Bayesian Parameters Estimation of Gamma distribution at Various Loss Functions
Author(s) -
Huda A. Rasheed,
Loaiy F. Naji
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1897/1/012014
Subject(s) - mathematics , bayes estimator , estimator , prior probability , bayes' theorem , invariant estimator , mean squared error , statistics , scale parameter , gamma distribution , bayesian probability , exponential function , exponential distribution , efficient estimator , minimum variance unbiased estimator , mathematical analysis
This paper deals with some Estimator of Bayes s of the parameters of Gamma distribution (GD) under three different loss functions, represented via Precautionary loss function, Entropy loss function (ELF) and invariant of scale squared error (SE) loss function, assuming Gamma and Exponential priors for the shape and scale parameters respectively. Maximum likelihood estimator (MLE) and Lindley’s approximation are used to obtain the Bayes estimates of the shape and scale parameters of GD. According to the Monte Carlo simulation method, those estimators have been compared based on the mean SEs (MSE’s). The results show that the performance of the Estimator of Bayes s under invariant of scale SE loss function produces the best estimates for the two parameters in all cases.