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

Derumigny Alexis () and Fermanian Jean-David ()
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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

Dependence Modeling, 2019, vol. 7, issue 1, 292-321

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.

Keywords: conditional dependence measures; kernel smoothing; conditional Kendall’s tau (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:7:y:2019:i:1:p:292-321:n:16

DOI: 10.1515/demo-2019-0016

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