Non-parametric estimators of multivariate extreme dependence functions

Belkacem Abdous, Kilani Ghoudi

    Research output: Contribution to journalArticlepeer-review

    13 Citations (Scopus)

    Abstract

    This article reviews various characterizations of a multivariate extreme dependence function A(). The most important estimators derived from these characterizations are also sketched. Then, a unifying approach, which puts all these estimators under the same framework, is presented. This unifying approach enables us to construct new estimators and, most importantly, to propose an automatic selection method for an unknown parameter on which all the existing non-parametric estimators of A() depend. Constrained smoothing splines and convex hull techniques are used to force the obtained estimators to be extreme dependence functions. A simulation study comparing these estimators on a wide range of extreme dependence functions is provided.

    Original languageEnglish
    Pages (from-to)915-935
    Number of pages21
    JournalJournal of Nonparametric Statistics
    Volume17
    Issue number8
    DOIs
    Publication statusPublished - Dec 1 2005

    Keywords

    • Constrained spline
    • Extreme dependence functions
    • Kernel estimator
    • Optimal bandwidth selection

    ASJC Scopus subject areas

    • Statistics and Probability
    • Statistics, Probability and Uncertainty

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