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Tail dependence models for distributions attracted to a max-stable law arefitted using observations above a high threshold. To cope with spatial,high-dimensional data, a rank-based M-estimator is proposed relying onbivariate margins only. A data-driven weight matrix is used to minimize theasymptotic variance. Empirical process arguments show that the estimator isconsistent and asymptotically normal. Its finite-sample performance is assessedin simulation experiments involving popular max-stable processes perturbed withadditive noise. An analysis of wind speed data from the Netherlands illustratesthe method.

An M-estimator of spatial tail dependence