On Kendall's regression

JOURNAL OF MULTIVARIATE ANALYSIS

卷:178

Article

Derumigny, Alexis¹  Fermanian, Jean-David² 

[1] Univ Twente, 5 Drienerlolaan, NL-7522 NB Enschede, Netherlands

[2] Crest Ensae, 5 Av Henry le Chatelier, F-91764 Palaiseau, France

关键词: Conditional dependence measures;    Conditional Kendall's tau;    Kernel smoothing;    Regression-type models;   

DOI  :  10.1016/j.jmva.2020.104610

来源: Elsevier

【 摘 要 】

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. (C) 2020 Elsevier Inc. All rights reserved.

【 授权许可】

Free