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

On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic... AbstractWe 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Dependence Modeling de Gruyter

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

Dependence Modeling , Volume 7 (1): 30 – Jan 1, 2019

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Publisher
de Gruyter
Copyright
© 2019 Alexis Derumigny et al., published by De Gruyter Open
ISSN
2300-2298
eISSN
2300-2298
DOI
10.1515/demo-2019-0016
Publisher site
See Article on Publisher Site

Abstract

AbstractWe 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.

Journal

Dependence Modelingde Gruyter

Published: Jan 1, 2019

References