An M-Estimator For Tail Dependence In Arbitrary Dimensions
(2011) , 23 pages
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- Authors
- Abstract
- 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 minimizes 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.
- Affiliations
Tilburg University and CentER University of Göttingen Institute for Mathematical Stochastics
Citations
EINMAHL, J. H. J., KRAJINA, A., & Segers, J. (2011). An M-Estimator For Tail Dependence In Arbitrary Dimensions (ISBA Discussion Paper 2011/05). https://hdl.handle.net/2078.5/208837