Estimating the Parameters of Mixture Gamma Distributions Using Maximum Likelihood and Bayesian Method

This paper focuses on the mixture Gamma distribution and uses the maximum likelihood and Bayesian techniques to estimate its parameters. This study uses Expectation Maximization Algorithm (EM) to find the maximum likelihood estimators and the random Metropolis-Hastings algorithm is used to simulate...

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Bibliographic Details
Published in:	المجلة العراقية للعلوم الاحصائية Vol. 21; no. 1; pp. 138 - 150
Main Authors:	Nagham Ibrahim Abdulla Najm, Raya Salim Al_Rassam
Format:	Journal Article
Language:	Arabic English
Published:	College of Computer Science and Mathematics, University of Mosul 01-06-2024
Subjects:	bayesian estimation expectation maximization algorithm gamma distribution likelihood function metropolis-hastings mixture distribution
Online Access:	Get full text
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Summary:	This paper focuses on the mixture Gamma distribution and uses the maximum likelihood and Bayesian techniques to estimate its parameters. This study uses Expectation Maximization Algorithm (EM) to find the maximum likelihood estimators and the random Metropolis-Hastings algorithm is used to simulate the Bayesian estimates of the parameters of mixture gamma distribution. then these estimates are compared by using the sum of the modulus of the bias (MBias), and the root-mean square error (RMSE). It has been shown that the Bayesian estimator is better than the maximum likelihood estimator.
ISSN:	1680-855X 2664-2956
DOI:	10.33899/iqjoss.2024.183254