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On kernel estimation of the second order rate parameter in multivariate extreme value statistics

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  • Goegebeur, Yuri
  • Guillou, Armelle
  • Qin, Jing

Abstract

We introduce a flexible class of kernel type estimators of a second order parameter appearing in the multivariate extreme value framework. Such an estimator is crucial in order to construct asymptotically unbiased estimators of dependence measures, as e.g. the stable tail dependence function. We establish the asymptotic properties of this class of estimators under suitable assumptions. The behaviour of some examples of kernel estimators is illustrated by a simulation study in which they are also compared with a benchmark estimator of a second order parameter recently introduced in the literature.

Suggested Citation

  • Goegebeur, Yuri & Guillou, Armelle & Qin, Jing, 2017. "On kernel estimation of the second order rate parameter in multivariate extreme value statistics," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 35-43.
    • Handle: RePEc:eee:stapro:v:128:y:2017:i:c:p:35-43
    DOI: 10.1016/j.spl.2017.04.015
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    References listed on IDEAS

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    1. Beirlant, Jan & Escobar-Bach, Mikael & Goegebeur, Yuri & Guillou, Armelle, 2016. "Bias-corrected estimation of stable tail dependence function," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 453-466.
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