A New Ridge-Type Estimator for the Gamma regression model

Authors

DOI:

https://doi.org/10.52866/ijcsm.2024.05.01.006

Keywords:

Multicollinearity, ridge estimator, gamma regression model, Liu-type estimator, Monte Carlo simulation

Abstract

 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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Published

2024-01-08

How to Cite

[1]
A. M. . Salih, Z. Algamal, and M. A. . Khaleel, “A New Ridge-Type Estimator for the Gamma regression model”, Iraqi Journal For Computer Science and Mathematics, vol. 5, no. 1, pp. 85–98, Jan. 2024.
CITATION
DOI: 10.52866/ijcsm.2024.05.01.006
Published: 2024-01-08

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Section

Articles