On kernel-based estimation of conditional Kendall's tau: finite-distance bounds and asymptotic behavior

Alexis Derumigny; Jean-David Fermanian

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arXiv · 1810.06234

On kernel-based estimation of conditional Kendall's tau: finite-distance bounds and asymptotic behavior

Alexis Derumigny·Jean-David Fermanian

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Abstract

We study nonparametric estimators of conditional Kendall's tau, a measure of concordance between two random variables given some covariates. We prove non-asymptotic bounds with explicit constants, that hold with high probabilities. We provide "direct proofs" of the consistency and the asymptotic law of conditional Kendall's tau. A simulation study evaluates the numerical performance of such nonparametric estimators.

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Alexis Derumigny, Jean-David Fermanian. 2018-10-15. On kernel-based estimation of conditional Kendall's tau: finite-distance bounds and asymptotic behavior. https://arxiv.org/abs/1810.06234

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