On kernel-based estimation of conditional Kendall's tau

Finite-distance bounds and asymptotic behavior

Journal article (2019)

Authors

Alexis Derumigny University of Twente
Jean David Fermanian CREST-ENSAE
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Published Date

2019

Language

English

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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.