About Kendall's regression

Alexis Derumigny, Jean-David Fermanian

Research output: Working paper

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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. We prove non-asymptotic bounds with explicit constants that hold with high probabilities. We derive the consistency of a two-step estimator, its asymptotic law and some oracle properties. Some simulations and applications to real data conclude the paper.
Original languageEnglish
Number of pages37
Publication statusPublished - 20 Nov 2018
Externally publishedYes


  • Conditional dependence measure
  • kernel smoothing
  • regression-type models
  • Conditional Kendall's tau


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