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

Alexis Derumigny, Jean-David Fermanian

Research output: Working paper

1 Downloads (Pure)

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.
Original languageEnglish
PublisherarXiv.org
Publication statusPublished - 15 Oct 2018
Externally publishedYes

Fingerprint

Kendall's tau
Nonparametric Estimator
Asymptotic Behavior
kernel
Concordance
Covariates
Random variable
Simulation Study
Evaluate

Keywords

  • Conditional dependence measure
  • kernel smoothing
  • Conditional Kendall's tau

Cite this

@techreport{8afa0f1758d040c7b3316c5c84929142,
title = "On kernel-based estimation of conditional Kendall's tau: finite-distance bounds and asymptotic behavior",
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 = "Conditional dependence measure, kernel smoothing, Conditional Kendall's tau",
author = "Alexis Derumigny and Jean-David Fermanian",
note = "29 pages, 4 figures. arXiv admin note: text overlap with arXiv:1802.07613",
year = "2018",
month = "10",
day = "15",
language = "English",
publisher = "arXiv.org",
type = "WorkingPaper",
institution = "arXiv.org",

}

TY - UNPB

T1 - On kernel-based estimation of conditional Kendall's tau

T2 - finite-distance bounds and asymptotic behavior

AU - Derumigny, Alexis

AU - Fermanian, Jean-David

N1 - 29 pages, 4 figures. arXiv admin note: text overlap with arXiv:1802.07613

PY - 2018/10/15

Y1 - 2018/10/15

N2 - 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.

AB - 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.

KW - Conditional dependence measure

KW - kernel smoothing

KW - Conditional Kendall's tau

M3 - Working paper

BT - On kernel-based estimation of conditional Kendall's tau

PB - arXiv.org

ER -