{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T04:45:03Z","timestamp":1790743503410,"version":"4.1.0"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2016,2,24]],"date-time":"2016-02-24T00:00:00Z","timestamp":1456272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,3,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>Consider the extreme quantile region induced by the half-space depth function HD of the form Q={x\u2208Rd:HD(x,P)\u2a7d\u03b2}, such that PQ=p for a given, very small p &amp;gt; 0. Since this involves extrapolation outside the data cloud, this region can hardly be estimated through a fully non-parametric procedure. Using extreme value theory we construct a natural semiparametric estimator of this quantile region and prove a refined consistency result. A simulation study clearly demonstrates the good performance of our estimator. We use the procedure for risk management by applying it to stock market returns.<\/jats:p>","DOI":"10.1111\/rssb.12163","type":"journal-article","created":{"date-parts":[[2016,2,26]],"date-time":"2016-02-26T08:09:50Z","timestamp":1456474190000},"page":"449-461","source":"Crossref","is-referenced-by-count":24,"title":["Estimation of Extreme Depth-Based Quantile Regions"],"prefix":"10.1093","volume":"79","author":[{"given":"Yi","family":"He","sequence":"first","affiliation":[{"name":"Tilburg University \u00a0 The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John H. 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