Cross validation of ridge regression estimator in autocorrelated linear regression models

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dc.authoridSOKUT ACAR, Tugba/0000-0002-4444-1671
dc.authoridOzkale, M.Revan/0000-0001-7085-7403
dc.contributor.authorAcar, T. Sokut
dc.contributor.authorOzkale, M. R.
dc.date.accessioned2025-01-27T20:50:14Z
dc.date.available2025-01-27T20:50:14Z
dc.date.issued2016
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractIn this paper, we investigated the cross validation measures, namely OCV, GCV and Cp under the linear regression models when the error structure is autocorrelated and regressor data are correlated. The best performed ridge regression estimator is obtained by getting the optimal ridge parameter so as to minimize these measures. A Monte Carlo simulation study is given to see how the optimal ridge parameter is affected by autocorrelation and the strength of multicollinearity.
dc.identifier.doi10.1080/00949655.2015.1112392
dc.identifier.endpage2440
dc.identifier.issn0094-9655
dc.identifier.issn1563-5163
dc.identifier.issue12
dc.identifier.scopus2-s2.0-84946866137
dc.identifier.scopusqualityQ2
dc.identifier.startpage2429
dc.identifier.urihttps://doi.org/10.1080/00949655.2015.1112392
dc.identifier.urihttps://hdl.handle.net/20.500.12428/25446
dc.identifier.volume86
dc.identifier.wosWOS:000378717700011
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofJournal of Statistical Computation and Simulation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectAutocorrelation
dc.subjectridge regression
dc.subjectmulticollinearity
dc.subjectordinary cross validation
dc.subjectgeneralized cross validation
dc.subjectconceptual prediction
dc.titleCross validation of ridge regression estimator in autocorrelated linear regression models
dc.typeArticle

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