An M-Estimator for Tail Dependence in Arbitrary Dimensions

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

Research output: Working paperDiscussion paperOther research output

318 Downloads (Pure)


Consider a random sample in the max-domain of attraction of a multivariate extreme value distribution such that the dependence structure of the attractor belongs to a parametric model. A new estimator for the unknown parameter is defined as the value that minimises the distance between a vector of weighted integrals of the tail dependence function and their empirical counterparts. The minimisation problem has, with probability tending to one, a unique, global solution. The estimator is consistent and asymptotically normal. The spectral measures of the tail dependence models to which the method applies can be discrete or continuous. Examples demonstrate the applicability and the performance of the method.
Original languageEnglish
Place of PublicationTilburg
Publication statusPublished - 2011

Publication series

NameCentER Discussion Paper


  • asymptotic statistics
  • factor model
  • M-estimation
  • multivariate extremes
  • tail dependence


Dive into the research topics of 'An M-Estimator for Tail Dependence in Arbitrary Dimensions'. Together they form a unique fingerprint.

Cite this