An M-estimator of Spatial Tail Dependence

J.H.J. Einmahl, A. Kiriliouk, A. Krajina, J. Segers

Research output: Working paperDiscussion paperOther research output

518 Downloads (Pure)

Abstract

Tail dependence models for distributions attracted to a max-stable law are tted 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 nite-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
Place of PublicationTilburg
PublisherEconometrics
Number of pages26
Volume2014-021
Publication statusPublished - 10 Mar 2014

Publication series

NameCentER Discussion Paper
Volume2014-021

    Fingerprint

Keywords

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

Cite this

Einmahl, J. H. J., Kiriliouk, A., Krajina, A., & Segers, J. (2014). An M-estimator of Spatial Tail Dependence. (CentER Discussion Paper; Vol. 2014-021). Tilburg: Econometrics.