Robust nonparametric estimation of the conditional tail dependence coefficient

JOURNAL OF MULTIVARIATE ANALYSIS

卷:178

Article

Goegebeur, Yuri¹  Guillou, Armelle^2,3  Nguyen Khanh Le Ho¹  Qin, Jing¹ 

[1] Univ Southern Denmark, Dept Math & Comp Sci, Campusvej 55, DK-5230 Odense M, Denmark

[2] Univ Strasbourg, Inst Rech Math Avancee, UMR 7501, 7 Rue Rene Descartes, F-67084 Strasbourg, France

[3] CNRS, 7 Rue Rene Descartes, F-67084 Strasbourg, France

关键词: Coefficient of tail dependence;    Empirical process;    Local estimation;    Robustness;   

DOI  :  10.1016/j.jmva.2020.104607

来源: Elsevier

【 摘 要 】

We consider robust and nonparametric estimation of the coefficient of tail dependence in presence of random covariates. The estimator is obtained by fitting the extended Pareto distribution locally to properly transformed bivariate observations using the minimum density power divergence criterion. We establish convergence in probability and asymptotic normality of the proposed estimator under some regularity conditions. The finite sample performance is evaluated with a small simulation experiment, and the practical applicability of the method is illustrated on a real dataset of air pollution measurements. (C) 2020 Elsevier Inc. All rights reserved.

【 授权许可】

Free