A fast algorithm for computing distance correlation

Abstract

Classical dependence measures such as Pearson correlation, Spearman's $\rho$, and Kendall's $\tau$ can detect only monotonic or linear dependence. To overcome these limitations, Szekely et al.(2007) proposed distance covariance as a weighted $L_2$ distance between the joint characteristic function and the product of marginal distributions. The distance covariance is $0$ if and only if two random vectors $X$ and $Y$ are independent. This measure has the power to detect the presence of a dependence structure when the sample size is large enough. They further showed that the sample distance covariance can be calculated simply from modified Euclidean distances, which typically requires $\mathcal{O}(n^2)$ cost. The quadratic computing time greatly limits the application of distance covariance to large data. In this paper, we present a simple exact $\mathcal{O}(n\log (n))$ algorithm to calculate the sample distance covariance between two univariate random variables. The proposed method essentially consists of two sorting steps, so it is easy to implement. Empirical results show that the proposed algorithm is significantly faster than state-of-the-art methods. The algorithm's speed will enable researchers to explore complicated dependence structures in large datasets.

References in corpus (5)

• Measuring and testing dependence by correlation of distances
• Brownian distance covariance
• Equivalence of distance-based and RKHS-based statistics in hypothesis testing
• Large-Scale Kernel Methods for Independence Testing
• A Statistically and Numerically Efficient Independence Test based on Random Projections and Distance Covariance

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• High-Dimensional Independence Testing via Maximum and Average Distance Correlations
• Robust Distance Covariance
• Private measurement of nonlinear correlations between data hosted across multiple parties