Addressing the Autocorrelation Problem in the Poisson Regression Model: Theory and Numerical Illustrations
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| Publicado en: | Pakistan Journal of Statistics and Operation Research vol. 21, no. 1 (2025), p. 39 |
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| Otros Autores: | , |
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University of the Punjab, College of Statistical & Actuarial Science
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 022 | |a 1816-2711 | ||
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| 024 | 7 | |a 10.18187/pjsor.v21i1.3909 |2 doi | |
| 035 | |a 3180705083 | ||
| 045 | 2 | |b d20250101 |b d20250331 | |
| 084 | |a 154929 |2 nlm | ||
| 100 | 1 | |a Sultan, Mustafa Haitham |u University of Tunis el manar, faculty of sciences of Tunis, Tunis | |
| 245 | 1 | |a Addressing the Autocorrelation Problem in the Poisson Regression Model: Theory and Numerical Illustrations | |
| 260 | |b University of the Punjab, College of Statistical & Actuarial Science |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Random errors | ||
| 653 | |a Autocorrelation | ||
| 653 | |a Regression models | ||
| 653 | |a Poisson density functions | ||
| 653 | |a Monte Carlo simulation | ||
| 653 | |a Dependent variables | ||
| 653 | |a Aircraft | ||
| 653 | |a Mean square errors | ||
| 653 | |a Simulation | ||
| 653 | |a Regression analysis | ||
| 653 | |a Random variables | ||
| 653 | |a Binomial distribution | ||
| 653 | |a Researchers | ||
| 653 | |a Methods | ||
| 700 | 1 | |a Amri, Fethi |u Unit of Research 3E, Higher Institute of Management of Gabes (I.S.G.), University of Gabes, Gabes, Tunisia | |
| 700 | 1 | |a Hamed, Mohamed S |u Department of Business Administration, Gulf Colleges, KSA | |
| 773 | 0 | |t Pakistan Journal of Statistics and Operation Research |g vol. 21, no. 1 (2025), p. 39 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3180705083/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3180705083/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |