MODEL SELECTION USING AN INFORMATION THEORY APPROACH

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dc.contributor.advisorWhite, Luther W
dc.creatorSHAQLAIH, ALI SALEH
dc.date.accessioned2019-05-01T17:23:32Z
dc.date.available2019-05-01T17:23:32Z
dc.date.issued2010
dc.description.abstractIn this thesis we use the information theoretic approach in selecting the best
dc.description.abstractmodel among many candidate models. It is shown that the information theoretic
dc.description.abstractapproach is better than the standard R2 approach in selecting models. We use
dc.description.abstractAkaike Information Criteria (AIC) to select the best model for resilient modulus
dc.description.abstractof a soil and for a girder. This approach is applied to statistical models, neural
dc.description.abstractnetwork models and physics based models. The information theory approach
dc.description.abstractis compared with the R2 approach and it is found that the information theo-
dc.description.abstractretic approach is more stable and gives better results. The notion of ranking
dc.description.abstractstability is introduced and is used as one of the reasons that makes information
dc.description.abstracttheory approach better than the R2 approach. Important results are captured
dc.description.abstractand compared to the results of the R2 method in two dierent data sets.
dc.description.abstractx
dc.format.extent110 pages
dc.format.mediumapplication.pdf
dc.identifier99119885702042
dc.identifier.urihttps://hdl.handle.net/11244/319435
dc.languageen_US
dc.relation.requiresAdobe Acrobat Reader
dc.subjectInformation theory
dc.subjectMathematical models
dc.subjectMathematical statistics
dc.thesis.degreePh.D.
dc.titleMODEL SELECTION USING AN INFORMATION THEORY APPROACH
dc.typetext
dc.typedocument
ou.groupCollege of Arts and Sciences::Department of Mathematics

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