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Effectiveness of neural networks to regression with structural changes
Author(s) -
Asano Miyoko,
Tsubaki Hiroe,
Yoshizawa Tadashi
Publication year - 2002
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.471
Subject(s) - artificial neural network , regression , nonparametric regression , smoothing , scatter plot , regression analysis , econometrics , computer science , simple (philosophy) , parametric statistics , simple linear regression , function (biology) , artificial intelligence , statistics , mathematics , machine learning , philosophy , epistemology , evolutionary biology , biology
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.