Identifying outlying groups through residual analysis and its application to healthcare expenditure

Traditional regression analysis primarily aims to describe the overall relationship between variables, often overlooking unexplainable aspects by design. Our focus is on these unexplained aspects, leveraging them to identify disparity groups with outlying behavior that deviate from the established model. We introduce a data-driven method for identifying such groups using group studentized residuals, which we term the mean squared of external studentized residuals. We apply this method to investigate disparities within healthcare markets, examining healthcare purchasing behavior and identifying the characteristics of disparity groups.