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Addressing the Autocorrelation Problem in the Poisson Regression Model: Theory and Numerical Illustrations

I tiakina i:
I whakaputaina i:Pakistan Journal of Statistics and Operation Research vol. 21, no. 1 (2025), p. 39
Kaituhi matua: Sultan, Mustafa Haitham
Ētahi atu kaituhi: Amri, Fethi, Hamed, Mohamed S
I whakaputaina:
University of the Punjab, College of Statistical & Actuarial Science
Ngā marau:
Random errors
Autocorrelation
Regression models
Poisson density functions
Monte Carlo simulation
Dependent variables
Aircraft
Mean square errors
Simulation
Regression analysis
Random variables
Binomial distribution
Researchers
Methods
Urunga tuihono:Citation/Abstract
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Whakarāpopotonga:The Poisson regression model (PRM) is usually applied in the situations where 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 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.
ISSN:1816-2711
2220-5810
DOI:10.18187/pjsor.v21i1.3909
Puna:Advanced Technologies & Aerospace Database