Geometric representation of high dimension, low sample size data

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dc.contributor.authorHall, Peter
dc.contributor.authorMarron, J S
dc.contributor.authorNeeman, Amnon
dc.date.accessioned2015-12-13T22:51:39Z
dc.date.issued2005
dc.date.updated2015-12-11T10:46:23Z
dc.description.abstractHigh dimension, low sample size data are emerging in various areas of science. We find a common structure underlying many such data sets by using a non-standard type of asymptotics: the dimension tends to ∞ while the sample size is fixed. Our analysis s
dc.identifier.issn1369-7412
dc.identifier.urihttp://hdl.handle.net/1885/81185
dc.publisherAiden Press
dc.sourceJournal of the Royal Statistical Society Series B
dc.subjectKeywords: Chemometrics; Large dimensional data; Medical images; Microarrays; Multivariate analysis; Non-standard asymptotics
dc.titleGeometric representation of high dimension, low sample size data
dc.typeJournal article
local.bibliographicCitation.issue3
local.bibliographicCitation.lastpage444
local.bibliographicCitation.startpage427
local.contributor.affiliationHall, Peter, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationMarron, J S, University of North Carolina
local.contributor.affiliationNeeman, Amnon, College of Physical and Mathematical Sciences, ANU
local.contributor.authoruidHall, Peter, u7801145
local.contributor.authoruidNeeman, Amnon, u9903889
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor010405 - Statistical Theory
local.identifier.ariespublicationMigratedxPub9535
local.identifier.citationvolume67
local.identifier.doi10.1111/j.1467-9868.2005.00510.x
local.identifier.scopusID2-s2.0-20744451888
local.type.statusPublished Version

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