Using Bayes weights to estimate parameters of a Gamma Regression model.
Abstract
In this paper, we suggested to use the Bayes approach in calculating the Bayes weights to treat the heterogeneity problem when estimating the gamma regression model parameters depending on the weighted least squares method and iterative weighted least squares method. A comparison with the classical method through an experimental side to simulate the generated data from a gamma distribution is presented. The data is analyzed through a MATLAB code designed for this purpose, in addition to the statistical program SPSS-25 and EasyFit-5.5. The aims of this study are: solving of heteroscedasticity problem random error variance for gamma regression model by a proposed method which depends on Bayes weighted and estimation of the best fit gamma regression model by using Bayes weighted, as well as a comparison between the results from the classical and proposed methods through some statistical criteria, the results provided the preference of the proposed method on the classical method.
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