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Liu-type estimator for the gamma regression model
(Taylor & Francis Inc, 2020) Algamal, Zakariya Yahya; Asar, Yasin
In this paper, we propose a new biased estimator called Liu-type estimator in gamma regression models in the presence of collinearity. We also consider some other estimators such as ridge estimator and Liu estimator and conduct a Monte Carlo simulation study to compare the estimators under different designs of collinearity. Moreover, we provide a real data application to show the applicability of the new estimator. The simulations and real data results show that the proposed estimator outperforms better than other competitor estimators by yielding smaller mean squared error (MSE).