A Modified Jackknife Liu-Type Estimator for the Gamma Regression Models Data

Authors

  • Ahmed Mutlag Algboory College of Physical Education and Sport Sciences, University of Samarra, Iraq
  • Ahmed Naziyah Alkhateeb Department of Operation Research and Intelligent Techniques, University of Mosul, Iraq
  • Zakariya Yahya Algamal Department of Statistical Information, University of Mosul, Iraq

DOI:

https://doi.org/10.19139/soic-2310-5070-2304

Keywords:

Jackknife Liu-type, Gamma Regression, Multicollinearity, Monte Carlo Simulation

Abstract

Additional methods were suggested to enhance the biased estimation in the multiple linear regression model. The jackknife-biased estimate approach is essential for addressing high variance and multicollinearity issues. Reduce the effects of multicollinearity with the Liu estimator: This shrinkage method is attractive on several occasions. This document aims to derive a Jackknifed Liu-type Gamma estimator~(JGLTE) and a Modified Jackknifed Liu-type Gamma estimator~(MJGLTE) when multicollinearity exists. Based on Monte Carlo simulations, the proposed estimate outperforms the maximum likelihood estimator (MLE) in terms of mean square error (MSE). Finally, we illustrate the performance of this estimator using real-world data.

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Published

2025-07-04

Issue

Vol. 14 No. 5 (2025)

Section

Research Articles

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How to Cite

A Modified Jackknife Liu-Type Estimator for the Gamma Regression Models Data. (2025). Statistics, Optimization & Information Computing, 14(5), 2142-2154. https://doi.org/10.19139/soic-2310-5070-2304