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dc.contributor.authorMazumder, Rahul
dc.date.accessioned2021-04-12T15:02:44Z
dc.date.available2021-04-12T15:02:44Z
dc.date.issued2020-12
dc.identifier.issn1941-7330
dc.identifier.issn1932-6157
dc.identifier.urihttps://hdl.handle.net/1721.1/130446
dc.description.abstractSince 1973, the U.S. State Department has been using electronic record systems to preserve classified communications. Recently, approximately 1.9 million of these records from 1973–77 have been made available by the U.S. National Archives. While some of these communication streams have periods witnessing an acceleration in the rate of transmission, others do not show any notable patterns in communication intensity. Given the sheer volume of these communications, far greater than what had been available until now, scholars need automated statistical techniques to identify the communications that warrant closer study. We develop a statistical framework that can identify from a large corpus of documents a handful that historians would consider more interesting. Our approach brings together techniques from nonparametric signal estimation, statistical hypothesis testing and modern optimization methods—leading to a set of tools that help us identify and analyze various geometrical aspects of the communication streams. Dominant periods of heightened activities, as identified through these methods, correspond well with historical events recognized by standard reference works on the 1970s.en_US
dc.language.isoen
dc.publisherInstitute of Mathematical Statisticsen_US
dc.relation.isversionof10.1214/20-AOAS1344en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleMining events with declassified diplomatic documentsen_US
dc.typeArticleen_US
dc.identifier.citationGao, Yuanjun et al. “Mining events with declassified diplomatic documents.” Annals of Applied Statistics, 14, 4 (December 2020): 1699 - 1723 © 2020 The Author(s)en_US
dc.relation.journalAnnals of Applied Statisticsen_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-04-06T17:15:09Z
dspace.orderedauthorsGao, Y; Goetz, J; Connelly, M; Mazumder, Ren_US
dspace.date.submission2021-04-06T17:15:10Z
mit.journal.volume14en_US
mit.journal.issue4en_US
mit.licenseOPEN_ACCESS_POLICY


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