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

Alexis Derumigny*, Jean-David Fermanian

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

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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 pointwise and uniform bounds, 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. An application to the dependence between energy consumption and temperature conditionally to calendar days is finally provided.

    Original languageEnglish
    Pages (from-to)292-321
    Number of pages30
    JournalDependence Modeling
    Issue number1
    Publication statusPublished - Feb 2019


    • Conditional Kendall's tau
    • Conditional dependence measures
    • Kernel smoothing

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