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Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorYanev, Petko I.-
dc.contributor.authorKontoghiorghes, Erricos John-
dc.date.accessioned2013-01-28T08:19:22Zen
dc.date.accessioned2013-05-16T08:22:21Z-
dc.date.accessioned2015-12-02T09:32:18Z-
dc.date.available2013-01-28T08:19:22Zen
dc.date.available2013-05-16T08:22:21Z-
dc.date.available2015-12-02T09:32:18Z-
dc.date.issued2009-
dc.identifier.citationJournal of Computational and Graphical Statistics, 2009, vol. 18, no. 4, pp. 894-914en_US
dc.identifier.issn15372715-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/1977-
dc.description.abstractAn efficient graph-based strategy for performing the exhaustive k-fold crossvalidation procedure is proposed. All training (and testing) subsets are presented as nodes of a complete weighted graph. The arcs between the nodes indicate the different possibilities for deriving the solution of the destination node given the solution of the source node. The weights of the arcs represent the complexities of (the numerical operations involved in) updating and downdating the corresponding data matrices. The complete graph with arcs connecting every pair of nodes is defined and its properties are investigated. The optimum way of performing the exhaustive k-fold cross-validation is equivalent in deriving the path within the graph that has the minimum computational complexity. Furthermore, a generalization of the complete k-fold cross-validation graph is used to derive new strategies for performing random k-fold cross-validations. The proposed strategies generate additional nodes during the computations, which are part of the generalized graph. The additional nodes represent new models which have not been required initially, but provide additional information about the evaluated model. The advantages and the drawbacks of the proposed strategies are discussed. Numerical results are presented and analyzed. Finally the computation of all nearest neighbors of a given node is also considered. The Fortran 90 source code for the algorithms in the manuscript is available on-line.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Computational and Graphical Statisticsen_US
dc.rights© Taylor & Francisen_US
dc.subjectDowndatingen_US
dc.subjectModel evaluationen_US
dc.subjectModel selectionen_US
dc.subjectQR decompositionen_US
dc.subjectResamplingen_US
dc.subjectUpdatingen_US
dc.titleGraph-based strategies for performing the exhaustive and random k-fold cross-validationsen_US
dc.typeArticleen_US
dc.affiliationCyprus University of Technologyen
dc.collaborationUniversité de Neuchâtelen_US
dc.collaborationUniversity of Cyprusen_US
dc.collaborationUniversity of Londonen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.journalsSubscriptionen_US
dc.countrySwitzerlanden_US
dc.countryCyprusen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1198/jcgs.2009.08019en_US
dc.dept.handle123456789/54en
dc.relation.issue4en_US
dc.relation.volume18en_US
cut.common.academicyear2009-2010en_US
dc.identifier.spage894en_US
dc.identifier.epage914en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.journal.journalissn1537-2715-
crisitem.journal.publisherTaylor & Francis-
crisitem.author.deptDepartment of Finance, Accounting and Management Science-
crisitem.author.facultyFaculty of Tourism Management, Hospitality and Entrepreneurship-
crisitem.author.orcid0000-0001-9704-9510-
crisitem.author.parentorgFaculty of Management and Economics-
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