A Bias-Reduced Estimator for Negative Binomial Regression with an Application to CO2 Emissions Data
Fatimah M. Alghamdi, Gamal A. Abd-Elmougod, M. A. El-Qurashi, Ehab M. Almetwally, Ahmed M. Gemeay and Ali T. Hammad* Author for corresponding; e-mail address: ali.taha@science.tanta.edu.eg
Volume :Vol.52 No.6 (November 2025)
Research Article
DOI: https://doi.org/10.12982/CMJS.2025.085
Received: 19 June 2025, Revised: 2 September 2025, Accepted: 9 September 2025, Published: 29 October 2025
Citation: Alghamdi F.M., Abd-Elmougod G.A., El-Qurashi M.A., Almetwally E.M., Gemeay A.M. and Hammad A.T., A bias-reduced estimator for negative binomial regression with an application to CO2 emissions data. Chiang Mai Journal of Science, 2025; 52(6): e2025085. DOI 10.12982/CMJS.2025.0085.
Graphical Abstract
Abstract
The negative binomial regression model (NBRM) is a widely used approach for analyzing non-negative count data, particularly when overdispersion is present. Parameter estimation in this model typically relies on the maximum likelihood estimator (MLE), which can produce unstable and unreliable results under multicollinearity. To address this issue, we present a hybrid version of the Kibria-Lukman estimator adapted for NBRM. We evaluate the efficacy of our proposed estimator compared to established methods via simulation studies and a practical application for estimating CO₂ emissions from vehicles in Canada. Our results show that the hybrid Kibria-Lukman estimator is more accurate and stable than traditional methods. This makes it a promising way to deal with multicollinearity in count data analysis.