Robust nonparametric estimation of the conditional tail dependence coefficient

Yuri Goegebeur, Armelle Guillou*, Nguyen Khanh Le Ho, Jing Qin

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

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.

Original languageEnglish
Article number104607
JournalJournal of Multivariate Analysis
Volume178
Number of pages20
ISSN0047-259X
DOIs
Publication statusPublished - Jul 2020

Keywords

  • Coefficient of tail dependence
  • Empirical process
  • Local estimation
  • Robustness

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