Asymptotic frameworks for high-dimensional two-group classification

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dc.contributor.author	Duarte Silva, A. P.
dc.date.accessioned	2016-12-23T11:28:19Z
dc.date.available	2016-12-23T11:28:19Z
dc.date.issued	2011
dc.description.abstract	Asymptotic properties of two-group supervised classi cation rules designed for problems with much more variables than observations are discussed. Two types of asymptotic bounds on expected error rates are considered: (i) bounds that assume consistent mean estimators and focus on the impact of the covariance matrix estimation. (ii) bounds that consider the errors in mean and covariance estimation. Known results for independence-based classi cation rules are generalized to correlationadjusted linear rules.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	DUARTE SILVA, A. P. - Asymptotic frameworks for high-dimensional two-group classification. - In 58th ISI World Statistics Congress, Dublin, Ireland, 21-26 August, 2011. – In Bulletin of the ISI 58th World Statistics Congress, Netherlands: International Statistical Institute, 2012. ISBN 978-90-73592-33-9. p. 4445-4449	pt_PT
dc.identifier.isbn	9789073592339
dc.identifier.uri	http://hdl.handle.net/10400.14/21163
dc.language.iso	eng	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.title	Asymptotic frameworks for high-dimensional two-group classification	pt_PT
dc.type	conference object
dspace.entity.type	Publication
rcaap.rights	openAccess	pt_PT
rcaap.type	conferenceObject	pt_PT

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