Distance measures and smoothing methodology for imputing features of documents

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dc.contributor.authorFeuerverger, Andrey
dc.contributor.authorTilahun, Gelila
dc.contributor.authorHall, Peter
dc.contributor.authorGervers, Michael
dc.date.accessioned2015-12-13T22:59:14Z
dc.date.issued2005
dc.date.updated2015-12-12T07:27:13Z
dc.description.abstractWe suggest a new class of metrics for measuring distances between documents, generalizing the well-known resemblance distance. We then show how to combine distance measures with statistical smoothing to develop techniques for imputing missing features of
dc.identifier.issn1061-8600
dc.identifier.urihttp://hdl.handle.net/1885/83676
dc.publisherAmerican Statistical Association
dc.sourceJournal of Computational and Graphical Statistics
dc.subjectKeywords: Bandwidth; Correspondence distance; Cross-validation; Dating; Kernel; Resemblance distance; Shingle
dc.titleDistance measures and smoothing methodology for imputing features of documents
dc.typeJournal article
local.bibliographicCitation.issue2
local.bibliographicCitation.lastpage262
local.bibliographicCitation.startpage255
local.contributor.affiliationFeuerverger, Andrey, University of Toronto
local.contributor.affiliationTilahun, Gelila, University of Toronto
local.contributor.affiliationHall, Peter, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationGervers, Michael, University of Toronto
local.contributor.authoremailrepository.admin@anu.edu.au
local.contributor.authoruidHall, Peter, u7801145
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor010405 - Statistical Theory
local.identifier.ariespublicationMigratedxPub11957
local.identifier.citationvolume14
local.identifier.doi10.1198/106186005X47291
local.identifier.scopusID2-s2.0-20744459118
local.identifier.uidSubmittedByMigrated
local.type.statusPublished Version

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