A New Two-parameter Estimator for the Gamma Regression Model
Abstract
In this paper, we propose a new two-parameter biased estimator in gamma regression models when there is collinearity among the regressors. We investigate the mean squared error (MSE) properties of the newly proposed estimator. Moreover, we provide some theorems to compare the new estimators to the existing ones. We conduct a Monte Carlo simulation study to compare the estimators under different designs of collinearity in the sense of MSE. Moreover, we provide a real data application to show the usefulness of the new estimator.The simulations and real data results show that the proposed estimator beats other competitor estimators.References
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