An M-estimator of spatial tail dependence

John Einmahl, Anna Kiriliouk, A. Krajina, J.J.J. Segers

Research output: Contribution to journalArticleScientificpeer-review

26 Citations (Scopus)


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 rankbased 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.
Original languageEnglish
Pages (from-to)275-298
Number of pages23
JournalJournal of the Royal Statistical Society Series B-Statistical Methodology
Issue number1
Publication statusPublished - Jan 2016


  • Brown-Resnick process
  • exceedances
  • multivariate extremes
  • ranks
  • spatial statistics
  • stable tail dependence function


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