MODEL SELECTION USING AN INFORMATION THEORY APPROACH
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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.
x
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.
x