Journal of Data Science

Abstract: Identifying influential observations is an important part of the model building process in linear regression. There are numerous diagnostic measures based on different approaches in linear regression analysis. However, the problem of multicollinearity and influential observations may occur simultaneously. Therefore, we propose new diagnostic measures based on the two parameter ridge estimator defined by Lipovetsky and Conklin (2005) alternative to the usual ridge regression and ordinary linear regression. We define two parameter ridge-type generalizations of DFFITS and Cook’s distance. Moreover, we obtain approximate case deletion formulas and provide approximate versions of new measures. Finally, we illustrate the benefits of proposed measures in real data examples.