Estimating a Multidimensional Extreme-Value Distribution
Let F and G be multivariate probability distribution functions, each with equal one dimensional marginals, such that there exists a sequence of constants an > 0, n [set membership, variant] , with [formula] for all continuity points (x1, ..., xd) of G. The distribution function G is characterized by the extreme-value index (determining the marginals) and the so-called angular measure (determining the dependence structure). In this paper, a non-parametric estimator of G, based on a random sample from F, is proposed. Consistency as well as asymptotic normality are proved under certain regularity conditions.
Year of publication: |
1993
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Authors: | Einmahl, J. H. J. ; Dehaan, L. ; Huang, X. |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 47.1993, 1, p. 35-47
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Publisher: |
Elsevier |
Saved in:
Online Resource
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