Two parameter Ridge estimator in the inverse Gaussian regression model

Abstract

It is well known that multicollinearity, which occurs among the explanatory variables, has adverse effects on the maximum likelihood estimator in the inverse Gaussian regression model. Biased estimators are proposed to cope with the multicollinearity problem in the inverse Gaussian regression model. The main interest of this article is to introduce a new biased estimator. Also, we compare newly proposed estimator with the other estimators given in the literature. We conduct a Monte Carlo simulation and provide a real data example to illustrate the performance of the proposed estimator over the maximum likelihood and Ridge estimators. As a result of the simulation study and real data example, the newly proposed estimator is superior to the other estimators used in this study.

Performance of the Ridge and Liu Estimators in the zero‐inflated Bell Regression Model

“…e generalized linear model is employed when the response variable does not follow a Gaussian (normal) distribution [1][2][3][4][5][6][7][8][9][10][11]. In modeling, data in the form of counts are usually common, especially in economics and medicine.…”

Inverse Gaussian Regression Modeling and Its Application in Neonatal Mortality Cases in Indonesia

“…[8] designed Generalized Linear Models (GLM)-based control charts when the dependent variable follows the inverse Gaussian distribution. Handling the multicollinearity cases in the IGR model was employed by [9], [10], [11], and [12].…”