A New Shrinkage Estimator for the Bell Regression Model

Authors

  • Sura Mohamed Jamal Alden Hussein Environment Researchers Center, University of Mosul, Mosul, Iraq
  • Zakariya Yahya Algamal Department of Statistics and Informatics, University of Mosul, Mosul, Iraq

Keywords:

Bell regression model, collinearity, count data, K-L estimator, over-dispersion, Monte Carlo simulation

Abstract

In real-world applications, collinearity can be problematic when modeling the link between the response variable and multiple explanatory variables. Collinearity in the Bell Regression Model (BRM), which is used for modeling count data with over-dispersion, presents a challenge because it makes the estimation unstable and inflates the variance of the parameter estimates. The Kibria and Lukman (K-L) estimator is one of shrinkage estimator. To model count data with over-dispersion, a variation of the K-L estimator is proposed in this paper for the BRM. The results of the Monte Carlo simulation and the Bell regression model application indicate that the suggested estimate significantly reduces the mean squared error when compared to other competing estimators.

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Published

2026-03-29

How to Cite

Mohamed Jamal Alden Hussein , S. ., & Yahya Algamal, Z. . (2026). A New Shrinkage Estimator for the Bell Regression Model . Thailand Statistician, 24(2), 462–473. retrieved from https://ph02.tci-thaijo.org/index.php/thaistat/article/view/264629

Issue

Vol. 24 No. 2 (2026): April

Section

Articles

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