| Title | Modeling extreme events: sample fraction adaptive choice in parameter estimation |
| Publication Type | Unpublished |
| Year of Publication | 2013 |
| Authors | Neves MM, Gomes IM, Figueiredo F, Gomes DP |
| Series Title | Preprint |
| Keywords | adaptive choice, extremal index, Extreme value index, sample fraction, Semi-parametric estimation |
| Abstract | When modeling extreme events there are a few primordial parameters, among which we refer the extreme value index and the extremal index. The extreme value index measures the right tail-weight of the underlying distribution and the extremal index characterizes the degree of local dependence in the extremes of a stationary sequence. Most of the semi-parametric estimators of these parameters shows the same type of behavior: nice asymptotic properties, but a high variance for small values of k, the number of upper order statistics used in the estimation, and a high bias for large values of k. This brings a real need for the choice of k. Choosing some well-known estimators of those two parameters we revisit the application of a heuristic algorithm for the adaptive choice of k. A simulation study illustrates the performance of the proposed algorithm. |
| URL | http://www.dm.fct.unl.pt/sites/www.dm.fct.unl.pt/files/preprints/2013/4_13.pdf |
