{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T11:19:09Z","timestamp":1774437549926,"version":"3.50.1"},"reference-count":6,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T00:00:00Z","timestamp":1611964800000},"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":[[2022,2,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>In this paper we investigate a divide-and-conquer algorithm for estimating the extreme value index when data are stored in multiple machines. The oracle property of such an algorithm based on extreme value methods is not guaranteed by the general theory of distributed inference. We propose a distributed Hill estimator and establish its asymptotic theories. We consider various cases where the number of observations involved in each machine can be either homogeneous or heterogeneous, and either fixed or varying according to the total sample size. In each case we provide a sufficient, sometimes also necessary, condition under which the oracle property holds.<\/jats:p>","DOI":"10.1093\/biomet\/asab001","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T15:21:54Z","timestamp":1610551314000},"page":"257-264","source":"Crossref","is-referenced-by-count":5,"title":["Distributed inference for the extreme value index"],"prefix":"10.1093","volume":"109","author":[{"given":"Liujun","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Statistics, Fudan University , 220 Handan Road, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deyuan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Statistics, Fudan University , 220 Handan Road, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Zhou","sequence":"additional","affiliation":[{"name":"Econometric Institute, Erasmus University Rotterdam , P.O. Box 1738, Rotterdam 3000 DR, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,1,30]]},"reference":[{"key":"2024031118282341700_B1","doi-asserted-by":"crossref","DOI":"10.1007\/0-387-34471-3","volume-title":"Extreme Value Theory: An Introduction","author":"de Haan,","year":"2006"},{"key":"2024031118282341700_B2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1111\/rssb.12099","article-title":"Statistics of heteroscedastic extremes","volume":"78","author":"Einmahl,","year":"2016","journal-title":"J. R. Statist. Soc. B"},{"key":"2024031118282341700_B3","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1214\/aos\/1176343247","article-title":"A simple general approach to inference about the tail of a distribution","volume":"3","author":"Hill,","year":"1975","journal-title":"Ann. 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