Mohamed Sewalim El-sayed Hamed
Lecturer
Addressing the Autocorrelation Problem in the Poisson Regression Model: Theory and Numerical Illustrations
Pakistan Journal of Statistics and Operation Research (PJSOR) • 2025
Publication Information
Authors
Sultan, M. H., Amri, F., & Hamed, M. S.
Keywords
Poisson Regression Model ،Autocorrelation Problem, KL Estimator, GLS Estimator, RLE Estimator, LE Estimator.
Journal
Pakistan Journal of Statistics and Operation Research (PJSOR)
Publisher
Pakistan Journal of Statistics and Operation Research (PJSOR)
Volume
21
Issue
1
Pages
39-50
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
The Poisson regression model (PRM) is usually applied when the dependent variable is in the form of count data. The purpose of this study is to compare methods of estimation for the Poisson Regression
Model's first-order autocorrelation (AR(1)). The Kibria and Lukman Estimator Method (KL), Generalized Least Square Estimator Method (GLS), the Liu Estimator Method (LE), and the Reduction Liu Estimator Method (RLE) were employed. Monte Carlo simulations are used to compare these methods. The data generated follows Poisson
Regression Model, however, because of sample size and autocorrelation levels among other things, to create a first-order
autocorrelation among random errors. The Mean square Error (MSE) criterion was used for comparison. The methods are also evaluated on actual data, moreover, the findings demonstrated that the KL approach is superior to the other estimation techniques in terms of its performance.
Model's first-order autocorrelation (AR(1)). The Kibria and Lukman Estimator Method (KL), Generalized Least Square Estimator Method (GLS), the Liu Estimator Method (LE), and the Reduction Liu Estimator Method (RLE) were employed. Monte Carlo simulations are used to compare these methods. The data generated follows Poisson
Regression Model, however, because of sample size and autocorrelation levels among other things, to create a first-order
autocorrelation among random errors. The Mean square Error (MSE) criterion was used for comparison. The methods are also evaluated on actual data, moreover, the findings demonstrated that the KL approach is superior to the other estimation techniques in terms of its performance.