Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/29449
Authors: 
Anders, Ulrich
Korn, Olaf
Year of Publication: 
1996
Series/Report no.: 
ZEW Discussion Papers 96-21
Abstract: 
In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification problems encountered. We then propose five specification strategies based on different statistical procedures and compare them in a simulation study. As the results of the study are promising, it is suggested that a statistical analysis should become an integral part of neural network modelling.
Subjects: 
Neural Networks
Statistical Inference
Model Selection
Identification
Information Criteria
Cross Validation
Document Type: 
Working Paper

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