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arXiv · 1802.07613

About Kendall's regression

Alexis Derumigny·Jean-David Fermanian
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Abstract

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.

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Alexis Derumigny, Jean-David Fermanian. 2018-02-21. About Kendall's regression. https://arxiv.org/abs/1802.07613

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