raed hsni, statistician, Ministry of Higher Education & Scientific Research
The Poisson regression model is one of the most important linear logarithmic regression models. It is the tool through which the dependent variable is ratios when its values are positive and in the form of ratios or countable data, as well as being the fitted model for analyzing rare events. Like other regression models, the explanatory variables included in its construction may be exposed to a high correlation between two or more variables, which negatively affects the process of estimating the model parameters.
In this presentation, we review the most prominent methods of estimating the parameters of the Poisson regression model when it suffers from a semi-perfect multilinearity problem, such as Ridge regression and Liu estimator's method. Estimation methods were applied to real data obtained from the Central Child Hospital in Baghdad, representing the number of cases of congenital defects of children in the heart and circulatory system from 2012-2019. The results showed the superiority of the (Liu) regression method over the Ridge regression method.
