Generalized Kibria-Lukman Estimator in the bell regression model

  • Farah Younus University of Mosul
  • Zinah ALbazzaz University of Mosul
  • Zakariya Algamal University of Mosul
DOI: https://doi.org/10.19139/soic-2310-5070-2791
Keywords: Collinearity, KL estimator, generalized KL estimator, Bell regression model, count data, Over-dispersion, Monte Carlo simulation

Abstract

Collinearity is an issue in a real-life implementation of the relationship between response variable and multiple explanatory variables. To preclude this problem, a number of shrinkage estimators of the linear regression model are conventionally offered. They include Kibria and Lukman estimator (KL). This paper is an extension of one of the estimators, that is, the KL estimator and generalization of KL estimator and optimal biasing parameter of our suggested estimator is deduced by minimizing the scalar mean squared error within the bell regression model to estimate the over-dispersed database. The result of the Monte Carlo simulation and application concerning Bell regression model indicates that the proposed estimator is much more an improvement compared to other competing estimators as far as the mean squared error is concerned.
Published
2025-12-12
How to Cite
Younus, F., ALbazzaz, Z., & Algamal, Z. (2025). Generalized Kibria-Lukman Estimator in the bell regression model. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-2791
Issue
Online First
Section
Research Articles
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