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dc.contributor.authorAydın, Dursun
dc.contributor.authorMammadov, Mammadagha
dc.date.accessioned2019-10-20T09:31:22Z
dc.date.available2019-10-20T09:31:22Z
dc.date.issued2012
dc.identifier.issn0233-1934
dc.identifier.issn1029-4945
dc.identifier.urihttps://dx.doi.org/10.1080/02331934.2011.574698
dc.identifier.urihttps://hdl.handle.net/11421/17675
dc.description24th Mini-EURO Conference on Continuous Optimization and Information-Based Technologies in the Financial Sector -- JUN 23-26, 2010 -- Izmir Univ Econom, Izmir, TURKEYen_US
dc.descriptionWOS: 000304274600008en_US
dc.description.abstractIn this article, we discuss the penalized least squares problem which has many advantageous computational properties. This method based on penalized spline (P-spline) smoothing can be formulated to fit into a linear mixed effects model framework. The most important issue in the implementation of this method is to specify the amount of smoothing. In an attempt to address the strategy of optimum amount of smoothing, this article provides a comparative study for different methods (or criteria) of choosing the optimum smoothing parameter: an improved version of the Akaike information criterion (AIC(c)); generalized cross-validation (GCV); cross-validation (CV); Mallows' C-p criterion; risk estimation using classical pilots (RECP) and restricted maximum likelihood (REML). In order to explore and compare the performance of these methods, a simulation study is performed for data sets with different sample sizes. As a result of simulation, the appropriate selection criteria are provided for a suitable smoothing parameter selection.en_US
dc.description.sponsorshipIzmir Chamber Commerce, Cent Bank Republ Turkey, European Off Aerosp Res & Dev (EOARD), EURO, Sci & Technol Res Council Turkey (TUBITAK), NETSIS Softwareen_US
dc.language.isoengen_US
dc.publisherTaylor & Francis LTDen_US
dc.relation.isversionof10.1080/02331934.2011.574698en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMixed Effect Modelen_US
dc.subjectPenalized Splineen_US
dc.subjectSmoothing Parameteren_US
dc.subjectCross-Validationen_US
dc.subjectGeneralized Cross-Validationen_US
dc.titleOptimum smoothing parameter selection for penalized least squares in form of linear mixed effect modelsen_US
dc.typeconferenceObjecten_US
dc.relation.journalOptimizationen_US
dc.contributor.departmentAnadolu Üniversitesi, Fen Fakültesi, İstatistik Bölümüen_US
dc.identifier.volume61en_US
dc.identifier.issue4en_US
dc.identifier.startpage459en_US
dc.identifier.endpage476en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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