Combating the Multicollinearity in Bell Regression Model: Simulation and Application
Keywords:
Bell regression, Liu, Multicollinearity, Poisson regression, RidgeAbstract
Poisson regression model has been popularly used to model count data. However, over-dispersion is a threat to the performance of the Poisson regression model. The Bell Regression Model (BRM) is an alternative means of modelling count data with over-dispersion. Conventionally, the parameters in BRM is popularly estimated using the Method of Maximum Likelihood (MML). Multicollinearity posed challenge on the efficiency of MML. In this study, we developed a new estimator to overcome the problem of multicollinearity. The theoretical, simulation and application results were in favor of this new method.
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Copyright (c) 2022 G. A. Shewa, F. I. Ugwuowo
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