A Liu estimator in inverse Gaussian regression model with application in chemometrics

Abstract

The presence of multicollinearity among the explanatory variables has undesirable effects on the maximum likelihood estimator (MLE). Liu estimator (LE) is a wide used estimator in overcoming this issue. The LE enjoys the advantage that its mean squared error (MSE) is less than MLE. The inverse Gaussian regression (IGR) model is a well-known model in application when the response variable positively skewed. The purpose of this paper is to derive the LE of the IGR under multicollinearity problem. In addition, the performance of this estimator is investigated under numerous methods for estimating the Liu parameter. Monte Carlo simulation results indicate that the suggested estimator performs better than the MLE estimator in terms of MSE. Furthermore, a real chemometrics dataset application is utilized and the results demonstrate the excellent performance of the suggested estimator when the multicollinearity is present in IGR model.