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

Author

Listed:
  • Derumigny Alexis

    (CREST-ENSAE and University of Twente, 5 Drienerlolaan, 7522 NB Enschede, Netherlands)

  • Fermanian Jean-David

    (CREST-ENSAE, 5, avenue Henry Le Chatelier, 91764Palaiseau cedex, France)

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 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.

Suggested Citation

  • Derumigny Alexis & Fermanian Jean-David, 2019. "On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic behavior," Dependence Modeling, De Gruyter, vol. 7(1), pages 292-321, January.
    • Handle: RePEc:vrs:demode:v:7:y:2019:i:1:p:292-321:n:16
    DOI: 10.1515/demo-2019-0016
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    References listed on IDEAS

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