# About Kendall's regression

arXiv: https://arxiv.org/abs/1802.07613 · HTML: https://mlsanity.com/paper/1802.07613/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2018.

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

- [TENDE: Transfer Entropy Neural Diffusion Estimation](https://mlsanity.com/paper/2510.14096/) (2025)

## Abstract (arXiv)

Conditional Kendall's tau is a measure of dependence between two random variables, conditionally on some covariates. We assume a regression-type relationship between conditional Kendall's tau and some covariates, in a parametric setting with a large number of transformations of a small number of regressors. This model may be sparse, and the underlying parameter is estimated through a penalized criterion. We prove non-asymptotic bounds with explicit constants that hold with high probabilities. We derive the consistency of a two-step estimator, its asymptotic law and some oracle properties. Some simulations and applications to real data conclude the paper.