A New Ridge-Type Estimator for the Gamma regression model
DOI:
https://doi.org/10.52866/ijcsm.2024.05.01.006Keywords:
Multicollinearity, ridge estimator, gamma regression model, Liu-type estimator, Monte Carlo simulationAbstract
When there is collinearity among the regressors in gamma regression models, we present a new
two-parameter ridge estimator in this study. We look into the new estimator's mean squared error characteristics.
Additionally, we offer several theorems to contrast the new estimators with the current ones. To compare the
estimators under various collinearity designs in terms of mean squared error, we run a Monte Carlo simulation
analysis. We also offer a real data application to demonstrate the usefulness of the new estimator. The results from
simulations and actual data reveal that the proposed estimator is superior to competing estimators.
Downloads
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Ahmed Maher Salih, Zakariya Algamal, Mundher Abdullah Khaleel
This work is licensed under a Creative Commons Attribution 4.0 International License.
Most read articles by the same author(s)
- Zakariya Algamal, Adewale Lukman, B. M. Kibria Golam, Arowolo Taofik, Modified Jackknifed Ridge Estimator in Bell Regression Model: Theory, Simulation and Applications , Iraqi Journal For Computer Science and Mathematics: Vol. 4 No. 1 (2023)
- Zakariya Algamal, Firas AL-Taie, Omar Qasim, Kernel semi-parametric model improvement based on quasi-oppositional learning pelican optimization algorithm , Iraqi Journal For Computer Science and Mathematics: Vol. 4 No. 2 (2023)