Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1151791 | Statistical Methodology | 2014 | 22 Pages |
Abstract
Estimation of extreme value copulas is often required in situations where available data are sparse. Parametric methods may then be the preferred approach. A possible way of defining parametric families that are simple and, at the same time, cover a large variety of multivariate extremal dependence structures is to build models based on spectral measures. This approach is considered here. Parametric families of spectral measures are defined as convex hulls of suitable basis elements, and parameters are estimated by projecting an initial nonparametric estimator on these finite-dimensional spaces. Asymptotic distributions are derived for the estimated parameters and the resulting estimates of the spectral measure and the extreme value copula. Finite sample properties are illustrated by a simulation study.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Jan Beran, Georg Mainik,