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A Goodness-of-fit Test for the Distribution Tail

Jean Diebolt 1 Myriam Garrido 2 Stéphane Girard 3 
3 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : In order to check that a parametric model provides acceptable tail approximations, we present a test which compares the parametric estimate of an extreme upper quantile with its semiparametric estimate obtained by extreme value theory. To build this test, the sampling variations of these estimates are approximated through parametric bootstrap. Numerical Monte Carlo simulations explore the covering probability and power of the test. A real-data study illustrates these results.
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  • HAL Id : hal-00814959, version 1
  • PRODINRA : 289937


Jean Diebolt, Myriam Garrido, Stéphane Girard. A Goodness-of-fit Test for the Distribution Tail. M. Ahsanullah and S.N.U.A. Kirmani. Topics in Extreme Values, Nova Science, New-York, pp.95-109, 2007, 978-1600217142. ⟨hal-00814959⟩



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