# Kendall's Tau for Functional Data Analysis

arXiv: https://arxiv.org/abs/1912.03725 · HTML: https://mlsanity.com/paper/1912.03725/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2019.

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

- [R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation](https://mlsanity.com/paper/2505.23493/) (2025)

## Abstract (arXiv)

We treat the problem of testing for association between a functional variable belonging to Hilbert space and a scalar variable. Particularly, we propose a distribution-free test statistic based on Kendall's Tau which is one of the most popular methods to determine the association between two random variables. The distribution of the test statistic under the null hypothesis of independence is established using the theory of U-statistics taking values in a Hilbert space. We also consider the case when the functional data is sparsely observed, a situation that arises in many applications. Simulations show that the proposed method outperforms the alternatives under several conditions demonstrating the robustness of our approach. We provide data applications that further consolidate the utility of our method.