01. A New D – K Class Estimator for the Poisson Regression Model: Simulation and Application

The Poisson regression model (PRM) aims to model a counting variable which is frequently estimated by using maximum likelihood estimation (MLE) method. Since the performance of MLE is not reliable when there exist a multicollinearity. Therefore, we proposed a new estimator called Dawoud – Kibria (DK) class estimator for the PRM as a solution to the problems caused by multicollinearity. For assessing the superiority of proposed estimator, we present a theoretical comparison with MLE, traditional ridge and Liu estimator that is based on matrix mean squared error (MMSE) and scalar mean squared error (MSE) criterions. A Monte Carlo simulation study is conducted under different controlled conditions in order to show the efficacy of the proposed estimator. An empirical application is also considered to see the clear image of the proposed DK estimator for the PRM. From the findings of simulation study and example it is observed that the DK class estimator is the most effective and consistent estimation method as compared to the MLE and other competitive estimators when there exist a multicollinearity issue.

Expert

Knowledge

Women in Statistics and Data Science 2024