Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/9916
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dc.contributor.authorGatu, Cristian-
dc.contributor.authorKontoghiorghes, Erricos John-
dc.contributor.otherΚοντογιώργης, Ερρίκος-
dc.date.accessioned2017-02-24T08:36:13Z-
dc.date.available2017-02-24T08:36:13Z-
dc.date.issued2013-04-01-
dc.identifier.citationStatistics and Computing, 2013, Volume 23, Issue 3, Pages 403-411en_US
dc.identifier.issn09603174-
dc.identifier.urihttp://ktisis.cut.ac.cy/handle/10488/9916-
dc.description.abstractAn efficient optimization algorithm for identifying the best least squares regression model under the condition of non-negative coefficients is proposed. The algorithm exposits an innovative solution via the unrestricted least squares and is based on the regression tree and branch-and-bound techniques for computing the best subset regression. The aim is to filling a gap in computationally tractable solutions to the non-negative least squares problem and model selection. The proposed method is illustrated with a real dataset. Experimental results on real and artificial random datasets confirm the computational efficacy of the new strategy and demonstrates its ability to solve large model selection problems that are subject to non-negativity constrains.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rights© 2012 Springer Science+Business Media, LLC.en_US
dc.subjectBranch-and-bound algorithmsen_US
dc.subjectSubset selectionen_US
dc.subjectNon-negative least squaresen_US
dc.titleA fast algorithm for non-negativity model selectionen_US
dc.typeArticleen_US
dc.doi10.1007/s11222-012-9318-8en_US
dc.collaborationUniversitatea Alexandru Ioan Cuzaen_US
dc.collaborationAustralian Catholic Universityen_US
dc.subject.categoryEconomics and Businessen_US
dc.journalsSubscription Journalen_US
dc.countryRomaniaen_US
dc.countryAustraliaen_US
dc.subject.fieldSocial Sciencesen_US
dc.publicationPeer Revieweden_US
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
crisitem.author.deptDepartment of Commerce, Finance and Shipping-
crisitem.author.facultyFaculty of Management and Economics-
crisitem.author.orcid0000-0001-9704-9510-
crisitem.author.parentorgFaculty of Management and Economics-
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