Date: 2015-09-11

Time: 15:30-16:30

Location: BURN 1205

Abstract:

The estimation of the extremal dependence structure of a multivariate extreme-value distribution is spoiled by the impact of the bias, which increases with the number of observations used for the estimation. Already known in the univariate setting, the bias correction procedure is studied in this talk under the multivariate framework. New families of estimators of the stable tail dependence function are obtained. They are asymptotically unbiased versions of the empirical estimator introduced by Huang (1992). Given that the new estimators have a regular behavior with respect to the number of observations, it is possible to deduce aggregated versions so that the choice of threshold is substantially simplified. An extensive simulation study is provided as well as an application on real data.

Speaker

Anne-Laure Fougères is a Professor in the Institut Camille-Jordan, Université Claude-Bernard, Lyon, France. Her main research interests are in extreme-value theory, multivariate data modeling, and functional estimation under shape constraints.