{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T18:01:49Z","timestamp":1790704909718,"version":"4.1.0"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2015,4,6]],"date-time":"2015-04-06T00:00:00Z","timestamp":1428278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Belgian Government","award":["P7\/06"],"award-info":[{"award-number":["P7\/06"]}]},{"name":"Belgian Fund for Scientific Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,1,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>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-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.<\/jats:p>","DOI":"10.1111\/rssb.12114","type":"journal-article","created":{"date-parts":[[2015,4,6]],"date-time":"2015-04-06T05:44:23Z","timestamp":1428299063000},"page":"275-298","source":"Crossref","is-referenced-by-count":32,"title":["An<i>M<\/i>-Estimator of Spatial Tail Dependence"],"prefix":"10.1093","volume":"78","author":[{"given":"John H. 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