An M-estimator of spatial tail dependence

John H.J. Einmahl, Anna Kiriliouk, Andrea Krajina, Johan Segers

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)275-298
Number of pages24
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume78
Issue number1
DOIs
Publication statusPublished - 1 Jan 2016
Externally publishedYes

Keywords

  • Brown–Resnick process
  • Exceedances
  • Multivariate extremes
  • Ranks
  • Spatial statistics
  • Stable tail dependence function

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