Restricted ride estimator in the Inverse Gaussian regression model

Abstract

The inverse Gaussian regression (IGR) model is a well-known model in application when the response variable positively skewed. Its parameters are usually estimated using maximum likelihood (ML) method. However, the ML method is very sensitive to multicollinearity. Ridge estimator was proposed in inverse gaussian regression model. A restricted ridge estimator is proposed. Simulation and real data example results demonstrate that the proposed estimator is outperformed ML and inverse Gaussian ridge estimator.