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Testing for lower tail dependence in extreme value models
Ist Teil von
Journal of statistical computation and simulation, 2019-09, Vol.89 (13), p.2583-2593
Ort / Verlag
Abingdon: Taylor & Francis
Erscheinungsjahr
2019
Link zum Volltext
Quelle
Taylor & Francis Journals Auto-Holdings Collection
Beschreibungen/Notizen
This paper proposes some tests for lower tail dependence in extreme value models. Let
be a random vector which follows in its lower tail a bivariate extreme value distribution with unit Frechet margins. We show that the conditional distribution function (df) of X+Y, given that X+Y <c, has a limiting df uniform on
, i.e.
, as
if and only if X, Y have a lower tail dependence. We recommend using Fisher's κ, Chi-square goodness-of-fit, Kolmogorov-Smirnov, Cramer-von Mises and Anderson-Darling tests for lower tail dependence. Simulations show that, except the Fisher's κ test, all tests have good performance in terms of the size and power. Finally, by using two real datasets, we illustrate the application of the proposed statistics in testing for lower tail dependence.