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dc.contributor.authorUcal, Meltem Şengün
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 linear
dc.description.abstractfor example the C-P method
dc.description.abstractthe Bayes Information Criterion (BIC) (Hannan-Quin)
dc.description.abstractthe Final Prediction Error Method (FPE lambda - Shibata. 1984)
dc.description.abstractAkaike Information Criterion (ACI)
dc.description.abstractSchwartz Criterion (SC)
dc.description.abstractCross-Validation (CV)
dc.description.abstractGeneralized Cross Validation (GCV) (Craven-Wahba)
dc.description.abstractLog Likelihood
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.
dc.publisherINT INST Informatics & Systemics
dc.subjectModel selection criteria
dc.subjectBootstrap selection criterion
dc.subjectCandidate for true model
dc.subjectMean squared prediction error
dc.subjectBootstrap estimator of the expected excess error
dc.titleParametric bootstrap model selection criterion with in linear model compared to other criteria
dc.typeProceedings Paper
dc.relation.journalWMSCI 2005: 9th World Multi-Conference on Systemics, Cybernetics and Informatics, Vol 8
dc.contributor.khasauthorUcal, Meltem Şengün

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