Model selection in neural networks

Ulrich Anders and Olaf Korn

No 96-21, ZEW Discussion Papers from ZEW - Leibniz Centre for European Economic Research

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.

Keywords: Neural Networks; Statistical Inference; Model Selection; Identification; Information Criteria; Cross Validation (search for similar items in EconPapers)
Date: 1996
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:zewdip:9621

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