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Relative performance of the statistical learning network: An application of the price-quality relationship in the automobile

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Date
2000-01
Dewey
Marketing
Sujet
Neural networks; Statistical learning networks; Hedonic prices; Connexionism
JEL code
Z13; L14; M11; M31
Journal issue
European Journal of Economic and Social Systems
Volume
14
Number
1
Publication date
01-2000
Article pages
69-79
Publisher
EDP sciences
DOI
http://dx.doi.org/10.1051/ejess:2000109
URI
https://basepub.dauphine.fr/handle/123456789/3581
Collections
  • DRM : Publications
Metadata
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Author
Desmet, Pierre
Type
Article accepté pour publication ou publié
Abstract (EN)
The design and topology of a neural network is still an important and difficult task. To solve the problems of topology posed by the introduction of connexionism, new approaches are proposed, and especially a combination of induction rules with a statistical estimation of the neuron coefficients for each layer. This research aims to compare an algorithm of this SLN approach with traditional methods (regression and classical BP neural networks) using the gradient method. Methods are put into application to determine the price-quality relationship of a complex product, the automobile, according to the hedonic price model. This application of the price-quality relationship to the English automobile market leads to the conclusion that the claimed superiority of this approach is unsubstantiated since, compared to the BP neural networks and even linear regression, the performance of the GMDH method is inferior.

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