A New Ridge-Type Estimator for the Gamma regression model

 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 curren...

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Main Authors: Ahmed Maher Salih, Zakariya Algamal, Mundher Abdullah Khaleel
Format: Article
Language:English
Published: College of Education, Al-Iraqia University 2024-01-01
Series:Iraqi Journal for Computer Science and Mathematics
Subjects:
Online Access:https://journal.esj.edu.iq/index.php/IJCM/article/view/812
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author Ahmed Maher Salih
Zakariya Algamal
Mundher Abdullah Khaleel
author_facet Ahmed Maher Salih
Zakariya Algamal
Mundher Abdullah Khaleel
author_sort Ahmed Maher Salih
collection DOAJ
description  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.
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spelling doaj.art-e2e647093ce8464ea0b1ffe23f8ebeb32024-01-08T23:05:35ZengCollege of Education, Al-Iraqia UniversityIraqi Journal for Computer Science and Mathematics2958-05442788-74212024-01-015110.52866/ijcsm.2024.05.01.006A New Ridge-Type Estimator for the Gamma regression modelAhmed Maher Salih0Zakariya Algamal1Mundher Abdullah Khaleel2Department of Mathematics , University of Tikrit, Tikrit, IraqUniversity of MosulDepartment of Mathematics , University of Tikrit, Tikrit, Iraq  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. https://journal.esj.edu.iq/index.php/IJCM/article/view/812Multicollinearityridge estimatorgamma regression modelLiu-type estimatorMonte Carlo simulation
spellingShingle Ahmed Maher Salih
Zakariya Algamal
Mundher Abdullah Khaleel
A New Ridge-Type Estimator for the Gamma regression model
Iraqi Journal for Computer Science and Mathematics
Multicollinearity
ridge estimator
gamma regression model
Liu-type estimator
Monte Carlo simulation
title A New Ridge-Type Estimator for the Gamma regression model
title_full A New Ridge-Type Estimator for the Gamma regression model
title_fullStr A New Ridge-Type Estimator for the Gamma regression model
title_full_unstemmed A New Ridge-Type Estimator for the Gamma regression model
title_short A New Ridge-Type Estimator for the Gamma regression model
title_sort new ridge type estimator for the gamma regression model
topic Multicollinearity
ridge estimator
gamma regression model
Liu-type estimator
Monte Carlo simulation
url https://journal.esj.edu.iq/index.php/IJCM/article/view/812
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