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.
- conditional dependence measures
- conditional Kendall's tau
- kernel smoothing
Derumigny, A. F. F. (Creator), Fermanian, J. D. (Contributor), Min, A. (Contributor) & van der Spek, R. A. J. (Contributor), TU Delft - 4TU.ResearchData, 2022