Model selection in neural networks

This article examines how model selection in neural networks can be guided by statistical procedures such as hypothesis tests, information criteria and cross validation, and proposes five specification strategies based on different statistical procedures.

Abstract

In this article, we examine how model selection in neural networks can be guided by statistical procedures such as hypothesis 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 modeling.