Almost Unbiased Liu Type Estimator in Bell Regression Model: Theory, Simulation and Application

In this paper, we gain the new almost unbiased Liu-type estimators to literature for the Bell regression model. We provide the superiority of the proposed estimator to its competitors such as the maximum likelihood estimator and Liu-type estimators via some theorems. We also design an extensive Monte Carlo simulation study to show that the proposed estimators outperforms the competitors in terms of mean squared error theoretically. Finally, we present a real data study to assess the performance of the introduced estimators in modeling real-life data. The findings of both the simulation and the empirical study demonstrate that the proposed regression estimators surpasses its competitors based on the mean square error criterion.