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

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 o...

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Bibliografski detalji
Glavni autori: Derumigny Alexis, Fermanian Jean-David
Format: Artigo
Jezik:Inglês
Izdano: De Gruyter 2019-09-01
Serija:Dependence Modeling
Teme:
Online pristup:https://doi.org/10.1515/demo-2019-0016
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