# Robust Distance Covariance

arXiv: https://arxiv.org/abs/2403.03722 · HTML: https://mlsanity.com/paper/2403.03722/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [Independent Component Analysis by Robust Distance Correlation](https://mlsanity.com/paper/2505.09425/) (2025)

## Abstract (arXiv)

Distance covariance is a popular measure of dependence between random variables. It has some robustness properties, but not all. We prove that the influence function of the usual distance covariance is bounded, but that its breakdown value is zero. Moreover, it has an unbounded sensitivity function, converging to the bounded influence function for increasing sample size. To address this sensitivity to outliers we construct a more robust version of distance correlation, which is based on a new data transformation. Simulations indicate that the resulting method is quite robust, and has good power in the presence of outliers. We illustrate the method on genetic data. Comparing the classical distance correlation with its more robust version provides additional insight.