About Kendall's regression

Alexis Derumigny, Jean-David Fermanian

Research output: Working paperProfessional

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. 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

Fingerprint

Kendall's tau
Covariates
Regression
Measures of Dependence
Oracle Property
Random variable
Estimator
Simulation
Model
Relationships

Keywords

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

Cite this

Derumigny, A., & Fermanian, J-D. (2018). About Kendall's regression.
Derumigny, Alexis ; Fermanian, Jean-David. / About Kendall's regression. 2018.
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About Kendall's regression. / Derumigny, Alexis; Fermanian, Jean-David.

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Research output: Working paperProfessional

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Derumigny A, Fermanian J-D. About Kendall's regression. 2018 Nov 20.