Parametric bootstrap model selection criterion with in linear model compared to other criteria

dc.contributor.authorUcal, Meltem Şengün
dc.date.accessioned2019-06-27T08:00:53Z
dc.date.available2019-06-27T08:00:53Z
dc.date.issued2005
dc.departmentFakülteler, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Ekonomi Bölümüen_US
dc.description.abstractThe most important stage of econometrics estimation is the in model set up. The model set up has the best prediction ability and is therefore suitable for econometrics estimation. Between the dependent variable Y and other independent explanatory variables X must be a strong relationship in econometrics estimation. However all explanatory variables cannot be related to dependent variable Y. This condition creates a regression problem. A similar problem appears in variable selection equivalent to problem in model selection. The most suitable faultless model is provided by correct and suitable selection of variables. There exist many variable/model selection procedures the where the necessary relationship between X and Y is linearen_US]
dc.description.abstractfor example the C-P methoden_US]
dc.description.abstractthe Bayes Information Criterion (BIC) (Hannan-Quin)en_US]
dc.description.abstractthe Final Prediction Error Method (FPE lambda - Shibata. 1984)en_US]
dc.description.abstractAkaike Information Criterion (ACI)en_US]
dc.description.abstractSchwartz Criterion (SC)en_US]
dc.description.abstractCross-Validation (CV)en_US]
dc.description.abstractGeneralized Cross Validation (GCV) (Craven-Wahba)en_US]
dc.description.abstractLog Likelihooden_US]
dc.description.abstractBootstrap and Jackknife. In this paper we compare some different model selection criteria with the parametric bootstrap and present a simple procedure to obtain a linear approximation of the mean squared prediction error. This study is based on empirical evidence and model training.en_US]
dc.identifier.citation0
dc.identifier.endpage357
dc.identifier.isbn978-980-6560-60-4
dc.identifier.scopus2-s2.0-84867374925en_US
dc.identifier.scopusqualityN/A
dc.identifier.startpage354en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/141
dc.identifier.wosWOS:000243687600068en_US
dc.identifier.wosqualityN/A
dc.institutionauthorUcal, Meltem Şengünen_US
dc.language.isoenen_US
dc.publisherINT INST Informatics & Systemicsen_US
dc.relation.journalWMSCI 2005: 9th World Multi-Conference on Systemics, Cybernetics and Informatics, Vol 8en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectModel selection criteriaen_US
dc.subjectBootstrap selection criterionen_US
dc.subjectCandidate for true modelen_US
dc.subjectMean squared prediction erroren_US
dc.subjectBootstrap estimator of the expected excess erroren_US
dc.titleParametric bootstrap model selection criterion with in linear model compared to other criteriaen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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