2014/03/08 by Einmahl, John, Kiriliouk, Anna, Krajina, Andrea +1
#62G32 #62H11 #FOS: Computer and information sciences #Methodology (stat.ME)
paper · doi:10.48550/arxiv.1403.1975
Tail dependence models for distributions attracted to a max-stable law are fitted using observations above a high threshold. To cope with spatial, high-dimensional data, a rank-based M-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.