Kibria–Lukman Hybrid Estimator for Handling Multicollinearity in Poisson Regression Model: Method and Application

Abstract

The Poisson regression model (PRM) is a widely used statistical technique for analyzing count data. However, when explanatory variables in the model are correlated, the estimation of regression coefficients using the maximum likelihood estimator (MLE) can be compromised by multicollinearity. This phenomenon leads to inaccurate parameter estimates, inflated variance, and increased mean squared error (MSE). To address multicollinearity in PRM, we propose a novel Kibria–Lukman hybrid estimator. We evaluate the performance of our estimator through extensive Monte Carlo simulations, assessing its accuracy using mean absolute percentage errors (MAPE) and MSE. Furthermore, we provide empirical applications to illustrate the practical relevance of our proposed method. Our simulation results and empirical applications demonstrate the superiority of the proposed estimator.