An M-estimator of Spatial Tail Dependence

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

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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

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wind velocity
matrix
simulation
experiment
distribution
analysis
method

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

An M-estimator of Spatial Tail Dependence. / Einmahl, J.H.J.; Kiriliouk, A.; Krajina, A.; Segers, J.

Tilburg : Econometrics, 2014. (CentER Discussion Paper; Vol. 2014-021).

Research output: Working paperDiscussion paperOther research output

TY - UNPB

T1 - An M-estimator of Spatial Tail Dependence

AU - Einmahl, J.H.J.

AU - Kiriliouk, A.

AU - Krajina, A.

AU - Segers, J.

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N2 - 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.

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KW - Brown-resnick process

KW - exceedances

KW - multivariate extremes

KW - ranks

KW - spatial statistics

KW - stable tail dependence function

M3 - Discussion paper

VL - 2014-021

T3 - CentER Discussion Paper

BT - An M-estimator of Spatial Tail Dependence

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Einmahl JHJ, Kiriliouk A, Krajina A, Segers J. An M-estimator of Spatial Tail Dependence. Tilburg: Econometrics. 2014 Mar 10. (CentER Discussion Paper).