Robust nonparametric estimation of the conditional tail dependence coefficient
| Autor: | Jing Qin, Nguyen Khanh Le Ho, Armelle Guillou, Yuri Goegebeur |
|---|---|
| Přispěvatelé: | Department of Mathematics and Computer Science [Odense] (IMADA), University of Southern Denmark (SDU), Institut de Recherche Mathématique Avancée (IRMA), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), ANR-19-CE40-0013,ExtremReg,Régression extrême avec applications à l'économétrie, l'environnement et à la finance(2019) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
Asymptotic distribution 02 engineering and technology robustness 01 natural sciences empirical process 010104 statistics & probability symbols.namesake [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] 0202 electrical engineering electronic engineering information engineering Applied mathematics local estimation Pareto distribution 0101 mathematics Divergence (statistics) Robustness Empirical process Mathematics Numerical Analysis Nonparametric statistics Tail dependence Estimator 020206 networking & telecommunications Convergence of random variables 13. Climate action Coefficient of tail dependence symbols Statistics Probability and Uncertainty Local estimation |
| Zdroj: | Goegebeur, Y, Guillou, A, Ho, N K L & Qin, J 2020, ' Robust nonparametric estimation of the conditional tail dependence coefficient ', Journal of Multivariate Analysis, vol. 178, 104607 . https://doi.org/10.1016/j.jmva.2020.104607 Journal of Multivariate Analysis Journal of Multivariate Analysis, Elsevier, 2020, 178, ⟨10.1016/j.jmva.2020.104607⟩ |
| ISSN: | 0047-259X 1095-7243 |
| DOI: | 10.1016/j.jmva.2020.104607 |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect