A Simple Method to Obtain Maximum Likelihood Estimates for Identity Poisson Regression Models

This paper aims to propose a simple method for obtaining MLE for identity link Poisson regression models. In order to estimate parameters of the models, fully parameterized models are suggested. An iterative fitting algorithm is then proposed for estimating parameters of the models. Through suitable reparameterization of the parameters in the models, the estimates of parameters in identity link Poisson regression models can be obtained easily. Because the method does not consider the subsets of the parameter space, computational burden is less intensive than the other methods. An example and simulation results are given to evaluate the performance of the suggested method.