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A Novel Regression Approach: Least Squares Ratio

dc.contributor.authorAkbilgic, Oguz
dc.contributor.authorAkinci, Eylem Deniz
dc.date.accessioned2025-01-09T20:12:07Z
dc.date.available2025-01-09T20:12:07Z
dc.date.issued2009
dc.identifier.issn0361-0926
dc.identifier.urihttps://doi.org/10.1080/03610920802455076
dc.identifier.urihttps://hdl.handle.net/20.500.14124/8391
dc.description.abstractRegression Analysis (RA) is one of the frequently used tool for forecasting. The Ordinary Least Squares (OLS) Technique is the basic instrument of RA and there are many regression techniques based on OLS. This paper includes a new regression approach, called Least Squares Ratio (LSR), and comparison of OLS and LSR according to mean square errors of estimation of theoretical regression parameters (mse ss) and dependent value (mse y).en_US
dc.language.isoengen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofCommunications in Statistics-Theory and Methodsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEstimationen_US
dc.subjectLeast squares ratioen_US
dc.subjectOrdinary least squaresen_US
dc.subjectPredictionen_US
dc.subjectRegressionen_US
dc.titleA Novel Regression Approach: Least Squares Ratioen_US
dc.typearticleen_US
dc.authoridDeniz, Eylem/0000-0001-8865-2086
dc.authoridakbilgic, oguz/0000-0003-0313-9254
dc.departmentMimar Sinan Güzel Sanatlar Üniversitesien_US
dc.identifier.doi10.1080/03610920802455076
dc.identifier.volume38en_US
dc.identifier.issue9en_US
dc.identifier.startpage1539en_US
dc.identifier.endpage1545en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.wosqualityQ4
dc.identifier.wosWOS:000265379500015
dc.identifier.scopus2-s2.0-70449625431
dc.identifier.scopusqualityQ2
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.snmzKA_20250105


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