A New Ridge-Type Estimator for the Gamma regression model

When there is collinearity among the regressors in gamma regression models, we present a newtwo-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 o...

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Bibliographic Details
Published in:	Iraqi Journal for Computer Science and Mathematics Vol. 5; no. 1; pp. 85 - 98
Main Authors:	Salih, Ahmed Maher, Algamal, Zakariya, Khaleel, Mundher Abdullah
Format:	Journal Article
Language:	English
Published:	College of Education, Al-Iraqia University 2024
Subjects:	gamma regression model Liu-type estimator Monte Carlo simulation Multicollinearity ridge estimator
Online Access:	Get full text
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Summary:	When there is collinearity among the regressors in gamma regression models, we present a newtwo-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 theestimators under various collinearity designs in terms of mean squared error, we run a Monte Carlo simulationanalysis. We also offer a real data application to demonstrate the usefulness of the new estimator. The results fromsimulations and actual data reveal that the proposed estimator is superior to competing estimators.
ISSN:	2958-0544 2788-7421
DOI:	10.52866/ijcsm.2024.05.01.006