On Kendall's regression

Alexis Derumigny, Jean David Fermanian

Research output: Contribution to journalArticleScientificpeer-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
JournalJournal of Multivariate Analysis
Volume178
DOIs
Publication statusPublished - Jul 2020
Externally publishedYes

Keywords

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

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