A new Liu-type estimator in inverse Gaussian regression model

Abstract

The ridge regression model has been shown to be an effective shrinking strategy for reducing the impacts of multicollinearity on a number of occasions. When the response variable is positively skewed, the inverse Gaussian regression model (IGR) is a popular model to use. Multicollinearity, on the other hand, is known to reduce the variance of the maximum likelihood estimator of inverse Gaussian regression coefficients. A inverse Gaussian ridge regression model (IGRR) has been presented as a solution to this problem. A novel estimator is proposed in this paper by presenting a generalization of the Liu-type estimator using IGR. In terms of absolute bias and mean squared error, our Monte Carlo simulation findings and real-world application demonstrate that the suggested estimator can provide significant improvements over other competing estimators.