1. Ktisis Cyprus University of Technology
  2. Cyprus University of Technology (Research Output)
  3. Άρθρα/Articles
Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/19427
DC FieldValueLanguage
dc.contributor.authorHofmann, Marc-
dc.contributor.authorGatu, Cristian-
dc.contributor.authorKontoghiorghes, Erricos John-
dc.contributor.authorColubi, Ana-
dc.contributor.authorZeileis, Achim-
dc.date.accessioned2020-11-18T09:49:41Z-
dc.date.available2020-11-18T09:49:41Z-
dc.date.issued2020-04-
dc.identifier.citationJournal of Statistical Software, 2020, vol. 93, no. 3en_US
dc.identifier.issn15487660-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/19427-
dc.description.abstractAn R package for computing the all-subsets regression problem is presented. The proposed algorithms are based on computational strategies recently developed. A novel algorithm for the best-subset regression problem selects subset models based on a pre-determined criterion. The package user can choose from exact and from approximation algorithms. The core of the package is written in C++ and provides an efficient implementation of all the underlying numerical computations. A case study and benchmark results illustrate the usage and the computational efficiency of the package.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Statistical Softwareen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectBest-subset regressionen_US
dc.subjectLinear regressionen_US
dc.subjectModel selectionen_US
dc.subjectRen_US
dc.subjectVariable selectionen_US
dc.titlelmSubsets: Exact Variable-Subset Selection in Linear Regression for Ren_US
dc.typeArticleen_US
dc.collaborationUniversity of Oviedoen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationUniversity of Iasien_US
dc.collaborationUniversity of Londonen_US
dc.collaborationUniversität Innsbrucken_US
dc.subject.categoryMathematicsen_US
dc.journalsOpen Accessen_US
dc.countryCyprusen_US
dc.countryRomaniaen_US
dc.countryUnited Kingdomen_US
dc.countryGermanyen_US
dc.countryAustriaen_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.18637/jss.v093.i03en_US
dc.relation.issue3en_US
dc.relation.volume93en_US
cut.common.academicyear2020-2021en_US
item.openairetypearticle-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.languageiso639-1en-
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-
crisitem.journal.journalissn1548-7660-
crisitem.journal.publisherUniversity of California Press-
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