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dc.contributor.advisor Fresen, J. L. Hildebrand, Annelize 2015-01-23T04:24:48Z 2015-01-23T04:24:48Z 1995-11
dc.identifier.citation Hildebrand, Annelize (1995) Model selection, University of South Africa, Pretoria, <> en
dc.description.abstract In developing an understanding of real-world problems, researchers develop mathematical and statistical models. Various model selection methods exist which can be used to obtain a mathematical model that best describes the real-world situation in some or other sense. These methods aim to assess the merits of competing models by concentrating on a particular criterion. Each selection method is associated with its own criterion and is named accordingly. The better known ones include Akaike's Information Criterion, Mallows' Cp and cross-validation, to name a few. The value of the criterion is calculated for each model and the model corresponding to the minimum value of the criterion is then selected as the "best" model. en
dc.format.extent 1 online resource (81 leaves) en
dc.language.iso en
dc.subject Model selection en
dc.subject Discrepancy measures en
dc.subject Criteria en
dc.subject Akaike Information Criterion en
dc.subject Mallows' Cp en
dc.subject Cross-validation en
dc.subject R-square en
dc.subject Adjusted R-square en
dc.subject Mean Square Error en
dc.subject.ddc 519.5
dc.subject.lcsh Mallows' Cp en
dc.subject.lcsh Akaike Information Criterion en
dc.subject.lcsh Mathematical models -- Evaluation en
dc.title Model selection en
dc.type Dissertation
dc.description.department Mathematical Sciences M. Sc. (Statistics)

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