Abstract

The maximal information coefficient (MIC) is a tool for finding the strongest pairwise relationships in a data set with many variables [1]. MIC is useful because it gives similar scores to equally noisy relationships of different types. This property, called equitability, is important for analyzing high-dimensional data sets. Here we formalize the theory behind both equitability and MIC in the language of estimation theory. This formalization has a number of advantages. First, it allows us to show that equitability is a generalization of power against statistical independence. Second, it allows us to compute and discuss the population value of MIC, which we call MIC∗. In doing so we generalize and strengthen the mathematical results proven in [1] and clarify the relationship between MIC and mutual information. Introducing MIC ∗ also enables us to reason about the properties of MIC more abstractly: for instance, we show that MIC∗ is continuous and that there is a sense in which it is a canonical “smoothing ” of mutual information. We also prove an alternate, equivalent characterization of MIC ∗ that we use to state new estimators of it as well as an algorithm for explicitly computing it when the joint probability density function of a pair of random variables is known. Our hope is that this paper provides a richer theoretical foundation for MIC and equitability going forward. This paper will be accompanied by a forthcoming companion paper that performs extensive empirical analysis and comparison to other methods and discusses the practical aspects of both equitability and the use of MIC and its related statistics.