AnM-estimator of spatial tail dependence
John H. J. Einmahl, Anna Kiriliouk, Andrea Krajina, Johan Segers
Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2016 · Volume 78 · Issue 1
Abstract
Tail dependence models for distributions attracted to a max-stable law are fitted by using observations above a high threshold. To cope with spatial, high dimensional data, a rank-basedM-estimator is proposed relying on bivariate margins only. A data-driven weight matrix is used to minimize the asymptotic variance. Empirical process arguments show that the estimator is consistent and asymptotically normal. Its finite sample performance is assessed in simulation experiments involving popular max-stable processes perturbed with additive noise. An analysis of wind speed data from the Netherlands illustrates the method.
Keywords
Brown–Resnick process, Exceedances, Multivariate extremes, Ranks, Spatial statistics, Stable tail dependence function
Enable JavaScript for citation tools and additional paper interactions.