A comparison between Bayesian Method and Full Maximum Likelihood to estimate Poisson regression model hierarchy and its application to the maternal deaths in Baghdad


Abstract: This research aims to compare Bayesian Method and Full Maximum Likelihood to estimate hierarchical Poisson regression model.The comparison was done by simulation using different sample sizes (n = 30, 60, 120) and different Frequencies (r = 1000, 5000) for the experiments as was the adoption of the Mean Square Error to compare the preference estimation methods and then choose the best way to appreciate model and concluded that hierarchical Poisson regression model that has been appreciated Full Maximum Likelihood Full Maximum Likelihood with sample size (n = 30) is the best to represent the maternal mortality data after it has been reliance value parameter to the distribution obtained through a program of (easy fit) (μ = 3.9167 ), and then we take the hypothetical values for this one smaller parameter (μ = 2.50) greater than the other (μ = 4.50) so as to obtain more accurate results, so it has been applied to real data that have been obtained from the Ministry of Health where he was recording the number of deaths mothers over five years and on a quarterly basis, were three circles healthier choice in Baghdad, since the validity of each circle represents the total will be so (20) watch for each group and the total aggregate Views will be (60).