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Effectiveness of neural networks to regression with structural changes

Effectiveness of neural networks to regression with structural changes This paper reports simple numerical experiments of the application of multi‐layered and feed‐forward neural networks to regression with change points to clarify one of the effectiveness of the neural network model compared with non‐parametric regression methods based on scatter plot smoothing. We also show an illustrative example, which successfully draws too rapid growth of GDP in Japan at the bubble economy around 1990 by interpreting decomposition of regression function suggested by the optimal neural networks fitting. Copyright © 2002 John Wiley & Sons, Ltd. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Stochastic Models in Business and Industry Wiley

Effectiveness of neural networks to regression with structural changes

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References (9)

Publisher
Wiley
Copyright
Copyright © 2002 John Wiley & Sons, Ltd.
ISSN
1524-1904
eISSN
1526-4025
DOI
10.1002/asmb.471
Publisher site
See Article on Publisher Site

Abstract

This paper reports simple numerical experiments of the application of multi‐layered and feed‐forward neural networks to regression with change points to clarify one of the effectiveness of the neural network model compared with non‐parametric regression methods based on scatter plot smoothing. We also show an illustrative example, which successfully draws too rapid growth of GDP in Japan at the bubble economy around 1990 by interpreting decomposition of regression function suggested by the optimal neural networks fitting. Copyright © 2002 John Wiley & Sons, Ltd.

Journal

Applied Stochastic Models in Business and IndustryWiley

Published: Jul 1, 2002

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