On Kendall's regression

Alexis Derumigny, Jean-David Fermanian*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Conditional Kendall's tau is a measure of dependence between two random variables, conditionally on some covariates. We assume a regression-type relationship between conditional Kendall's tau and some covariates, in a parametric setting with a large number of transformations of a small number of regressors. This model may be sparse, and the underlying parameter is estimated through a penalized criterion and a two-step inference procedure. We prove non-asymptotic bounds with explicit constants that hold with high probabilities. We derive the consistency of the latter estimator, its asymptotic law and some oracle properties. Some simulations and applications to real data conclude the paper.

Original languageEnglish
Article number104610
Number of pages22
JournalJournal of multivariate analysis
Volume178
Early online date23 Mar 2020
DOIs
Publication statusPublished - Jul 2020

Keywords

  • UT-Hybrid-D
  • Conditional Kendall's tau
  • Kernel smoothing
  • Regression-type models
  • Conditional dependence measures

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