Interpretation and Visualization of Distance Covariance through Additive Decomposition of Correlations Formula

Abstract

Distance covariance is a widely used statistical methodology for testing the dependency between two groups of variables. Despite the appealing properties of consistency and superior testing power, the testing results of distance covariance are often hard to be interpreted. This paper presents an elementary interpretation of the mechanism of distance covariance through an additive decomposition of correlations formula. Based on this formula, a visualization method is developed to provide practitioners with a more intuitive explanation of the distance covariance score.

Juan Du

Assistant Professor

My research interests include knowledge-infused data science for quality improvement, industrial data analytics and machine learning, and system informatics and control for manufacturing applications.