MODEL SELECTION USING AN INFORMATION THEORY APPROACH

Abstract

In this thesis we use the information theoretic approach in selecting the best

model among many candidate models. It is shown that the information theoretic

approach is better than the standard R2 approach in selecting models. We use

Akaike Information Criteria (AIC) to select the best model for resilient modulus

of a soil and for a girder. This approach is applied to statistical models, neural

network models and physics based models. The information theory approach

is compared with the R2 approach and it is found that the information theo-

retic approach is more stable and gives better results. The notion of ranking

stability is introduced and is used as one of the reasons that makes information

theory approach better than the R2 approach. Important results are captured

and compared to the results of the R2 method in two dierent data sets.

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