A new shrinkage estimator in Inverse Gaussian Regression model

Authors

  • Rafal Adeeb Othman, Farah Abdul Ghani Younus, Zakariya Yahya Algamal

Keywords:

Multicollinearity; ridge estimator; inverse Gaussian regression model; Monte Carlo simulation.

Abstract

The ridge estimator has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of multicollinearity. The inverse Gaussian regression model (INGRM) is a well-known model in application when the response variable is a skewed data. However, it is known that the variance of maximum likelihood estimator (MLE) of the INGRM coefficients can negatively affected in the presence of multicollinearity. In this paper, a new shrinkage estimator is proposed to overcome the multicollinearity problem in the INGRM. Our Monte Carlo simulation and real data application results suggest that the proposed estimator is better than the MLE estimator and ridge estimator, in terms of MSE.

Downloads

Published

2022-07-23