On kernel-based estimation of conditional Kendall's tau: finite-distance bounds and asymptotic behavior
Statistics Theory
2019-03-08 v2 Methodology
Statistics Theory
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 bounds with explicit constants, 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.
Keywords
Cite
@article{arxiv.1810.06234,
title = {On kernel-based estimation of conditional Kendall's tau: finite-distance bounds and asymptotic behavior},
author = {Alexis Derumigny and Jean-David Fermanian},
journal= {arXiv preprint arXiv:1810.06234},
year = {2019}
}
Comments
29 pages, 4 figures. arXiv admin note: text overlap with arXiv:1802.07613