A new Liu-type estimator in gamma regression model.

The model of ridge regression has ultimately been shown to be an effective shrinking strategy for reducing the impacts of multicollinearity on a number of occasions. As the variable of response positively skewed, the regression model of gamma (GR) is a popular model to use. Multicollinearity, on the other hand, is known to reduce the difference of the maximum possible estimator of coefficients of gamma regression. Truly, a regression model of gamma ridge model (GRR) has ultimately been presented as a solution to this problem. Moreover, a novel estimator is suggested in this paper by presenting a generalization of the Liu-type estimator using GR. Based on absolute bias as well as mean squared error, this Monte Carlo simulation findings along with real-world application demonstrate that the suggested estimator can provide real improvements over other competing estimators. [ABSTRACT FROM AUTHOR]