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An M ‐estimator of spatial tail dependence
Author(s) -
Einmahl John H. J.,
Kiriliouk Anna,
Krajina Andrea,
Segers Johan
Publication year - 2016
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/rssb.12114
Subject(s) - estimator , mathematics , bivariate analysis , rank (graph theory) , statistics , consistent estimator , spatial analysis , variance (accounting) , minimum variance unbiased estimator , statistical physics , physics , combinatorics , economics , accounting
Summary Tail dependence models for distributions attracted to a max‐stable law are fitted by using observations above a high threshold. To cope with spatial, high dimensional data, a rank‐based M ‐estimator is proposed relying on bivariate margins only. A data‐driven weight matrix is used to minimize the asymptotic variance. Empirical process arguments show that the estimator is consistent and asymptotically normal. Its finite sample performance is assessed in simulation experiments involving popular max‐stable processes perturbed with additive noise. An analysis of wind speed data from the Netherlands illustrates the method.

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