Empirical evaluation of the inverse Gaussian regression residuals for the assessment of influential points

Influential analysis is the main diagnostic process to obtain reliable regression results. Same is true for the generalized linear model. The present article empirically compares the performance of different residuals of the inverse Gaussian regression model to detect the influential points. The inverse Gaussian regression model residuals are further divided into two categories, that is, standardized and adjusted residuals. Cook's distance has been computed for both of the stated residuals, and then comparison of these residuals for the detection of influential point has been carried out with the help of simulation and a chemical related data set. The simulation results show that for small dispersion, the likelihood residuals are better than others and all the adjusted forms of residuals perform identically but not better than the standardized form. While for larger dispersion, all the standardized residuals perform in the same fashion, and they are better than the likelihood residuals for detection of influential points. Copyright (c) 2016 John Wiley & Sons, Ltd.