Improving the Efficiency of Regression Estimators in an Inverse Gaussian Regression Model

In this research study, the performance of estimation strategies was improved for an inverse Gaussian regression model in the presence of uncertainty regarding the quality of subspace information. It is suggested that the estimation strategies are based on linear shrinkage and shrinkage pretest. To evaluate the relative performance of these estimators, a Monte Carlo simulation for different combinations was conducted. The shrinkage pretest estimation strategy is suggested for use in practical situations, regardless of the quality of subspace information. Furthermore, suggested estimation strategies were applied to a real-data example to evaluate their practicality.