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

10/15/2018

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by   Alexis Derumigny, et al.

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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.