Combating the Multicollinearity in Bell Regression Model: Simulation and Application

G. A. Shewa; F. I. Ugwuowo

doi:10.46481/jnsps.2022.713

Authors

G. A. Shewa
[email protected]

Department of Mathematical Sciences, Taraba State University, Jalingo/Taraba, Nigeria
F. I. Ugwuowo
Department of Statistics, University of Nigeria, Nsukka/Enugu, Nigeria

Keywords:

Bell regression, Liu, Multicollinearity, Poisson regression, Ridge

Abstract

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.

Dimensions

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