About Kendall's regression

Alexis Derumigny () and Jean-David Fermanian ()
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Alexis Derumigny: CREST; ENSAE
Jean-David Fermanian: CREST; ENSAE

No 2018-01, Working Papers from Center for Research in Economics and Statistics

Abstract: Conditional Kendall's tau is a measure of dependence between two random variables, conditionally on some covariates. We study nonparametric estimators of such quantities using kernel smoothing techniques. Then, we assume a regression-type relationship between conditional Kendall's tau and covariates, in a parametric setting with possibly a large number of regressors. This model may be sparse, and the underlying parameter is estimated through a penalized criterion. The theoretical properties of all these estimators are stated. We prove non-asymptotic bounds with explicit constants that hold with high probability. We derive their consistency, their asymptotic law and some oracle properties. Some simulations and applications to real data conclude the paper.

Keywords: conditional dependence measures; kernel smoothing; regression-type models (search for similar items in EconPapers)
Pages: 60 pages
Date: 2018-02-21
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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