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Nonparametric Estimation of Multivariate Extreme Value Copulas with Known and Unknown Marginal Distributions
Author(s) -
Samia Ayari,
Mohamed Boutahar
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2068/1/012003
Subject(s) - estimator , nonparametric statistics , extreme value theory , empirical distribution function , mathematics , marginal distribution , multivariate statistics , generalized extreme value distribution , econometrics , statistics , monte carlo method , random variable
The purpose of this paper is estimating the dependence function of multivariate extreme values copulas. Different nonparametric estimators are developed in the literature assuming that marginal distributions are known. However, this assumption is unrealistic in practice. To overcome the drawbacks of these estimators, we substituted the extreme value marginal distribution by the empirical distribution function. Monte Carlo experiments are carried out to compare the performance of the Pickands, Deheuvels, Hall-Tajvidi, Zhang and Gudendorf-Segers estimators. Empirical results showed that the empirical distribution function improved the estimators’ performance for different sample sizes.

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