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Multivariate regression model selection from small samples using Kullback's symmetric divergence

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dc.contributor.authorSeghouane, Abd-Krim
dc.date.accessioned2015-12-07T22:19:30Z
dc.date.issued2006
dc.date.updated2015-12-07T08:36:50Z
dc.description.abstractThe Kullback Information Criterion, KIC, and its univariate bias-corrected version, KICc, are two new developed criteria for model selection. The two criteria can be viewed as estimators of the expected Kullback symmetric divergence and they have a fixed
dc.identifier.issn0165-1684
dc.identifier.urihttp://hdl.handle.net/1885/19381
dc.publisherElsevier
dc.sourceSignal Processing
dc.subjectKeywords: Asymptotic stability; Estimation; Mathematical models; Vectors; AIC; Kullback-Leibler information; Model selection; Multivariate regression model; Regression analysis AIC; KIC; KICc; Kullback-Leibler information; Model selection; Multivariate regression models
dc.titleMultivariate regression model selection from small samples using Kullback's symmetric divergence
dc.typeJournal article
local.bibliographicCitation.lastpage2084
local.bibliographicCitation.startpage2074
local.contributor.affiliationSeghouane, Abd-Krim, College of Engineering and Computer Science, ANU
local.contributor.authoruidSeghouane, Abd-Krim, u4593707
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor080611 - Information Systems Theory
local.identifier.absfor090609 - Signal Processing
local.identifier.ariespublicationu3357961xPUB8
local.identifier.citationvolume86
local.identifier.doi10.1016/j.sigpro.2005.10.009
local.identifier.scopusID2-s2.0-33744538807
local.type.statusPublished Version

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