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

Alexis Derumigny, Jean-David Fermanian

Research output: Working paper

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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.
Original languageEnglish
Publication statusPublished - 15 Oct 2018
Externally publishedYes



  • Conditional dependence measure
  • kernel smoothing
  • Conditional Kendall's tau

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