Liu-Type Estimator in Inverse Gaussian Regression Model Based on (r-(k-d)) Class Estimator

When multicollinearity arises in the inverse Gaussian regression (IGR), there is a substantially unstable variance in the maximum likelihood estimator. Based on the (r-(k-d)) class estimation method, we present a novel Liu-type estimator in the IGR model in this study. The study examines the e ectiveness of the suggested estimator and draws comparisons with alternative estimators. Based on simulation and real data results, the suggested estimate performs better than the other estimators in terms of mean squared error.