Influence diagnostics in the inverse Gaussian ridge regression model: Applications in chemometrics

Abstract

The influential observation affects the regression model inferences. Literature has shown that the problems of multicollinearity and influential observations can jointly exist in a model. The ridge regression estimator has been developed to handle the challenge of multicollinearity. The detection of influential observations with multicollinearity and its impact on the ridge estimates is necessary for better decision making. In this article, we proposed some influence diagnostics for the inverse Gaussian ridge regression model (IGRRM). The proposed diagnostics are evaluated with the help of a simulation study and two chemometric-related data sets. We found that the covariance ratio (CVR) method is better than other methods for the detection of influential observations under smaller dispersion. While for larger dispersion, all the IGRRM diagnostics perform equally well for the identification of influential observations.

Influence Diagnostic Methods in the Poisson Regression Model with the Liu Estimator

“…Khan et al [35] assessed the performance of influence diagnostics in the PRM with a ridge estimator. Recently, Khan et al [36] examined the superiority of influence diagnostics in the PRM with two-parameter estimator and, further, Amin et al [37] discussed the influence diagnostics for the inverse Gaussian ridge regression model. e available literature showed that no study in the GLM is available for influence diagnostics with the Liu estimator.…”