A note on AIC-type criteria, MDL principle and nonlinear model selection
Huang, Fuchun (2004) A note on AIC-type criteria, MDL principle and nonlinear model selection. In: Proceedings of The International Conference on Optimization: Techniques and Applications, 9 December 2004, Ballarat, Australia. (Unpublished)Full text for this resource is not available from the Research Repository.
By two examples this note illustrates that in nonlinear model selection it is essentially not the number of parameters but the total description length of the model, including the description length of the parameters, should be penalized in cooperating with the `goodness-of-¯t' of the model to the data. The fact illustrated is in favor of MDL principle rather than AIC-type criteria in nonlinear model selection.
|Item Type:||Conference or Workshop Item (Paper)|
|Uncontrolled Keywords:||AIC-type criteria, MDL principle, nonlinear regression, nonlinear autoregression, model selection|
|Subjects:||Faculty/School/Research Centre/Department > School of Engineering and Science
RFCD Classification > 280000 Information, Computing and Communication Sciences
|Depositing User:||Ms Phung T Tran|
|Date Deposited:||30 Oct 2008 21:51|
|Last Modified:||19 Sep 2011 04:22|
|ePrint Statistics:||View download statistics for this item|
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