Comparison Between Estimators Leu Regression Method and Ridge Regression Method of the Poisson Regression Model in The Presence of Multicollinearity Problem
DOI:
https://doi.org/10.31272/jae.i144.1236Keywords:
Poisson regression, Liu estimators, Ridge regression, Multicollinearity problemAbstract
The Poisson regression model is one of the most essential linear logarithmic regression models, and it is the tool through which the dependent variable is ratios when its values are positive and in the form of percentages or countable data, as well as the fitted model for analyzing the rare events. Like many other regression models, the predictor variables included in its construction may be exposed to a high correlation between two or more variables, negatively affecting the estimation of the model parameters. In this paper, we will review the most prominent methods for estimating parameters of the Poisson regression model when data suffers from a semi-multicollinearity problem, such as Ridge regression and Liu estimator's method. Estimation methods were applied to real data obtained from Central Child Hospital in Baghdad, representing the number of cases of congenital disabilities of children in the heart and circulatory system from 2012 to 2019. The results showed the superiority of the Liu estimators' method over the ridge regression method based on (AIC) as a criterion for comparison.
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