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

Alexis Derumigny, Jean David Fermanian

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

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 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.

Original languageEnglish
Pages (from-to)292-321
Number of pages30
JournalDependence Modeling
Volume7
Issue number1
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • conditional dependence measures
  • conditional Kendall's tau
  • kernel smoothing

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