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Principles of neural model identification, selection and adequacy : with applications to financial econometrics

Author: A D Zapranis; Apostolos-Paul Refenes
Publisher: London ; New York : Springer, ©1999.
Series: Perspectives in neural computing.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:

Neural networks have had considerable success in a variety of disciplines including engineering, control, and financial modelling.

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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: A D Zapranis; Apostolos-Paul Refenes
ISBN: 1852331399 9781852331399
OCLC Number: 40397003
Description: ix, 190 pages ; 24 cm.
Contents: 1. Introduction --
2. Neural Model Identification --
3. Review of Current Practice in Neural Model Identification --
4. Neural Model Selection: the Minimum Prediction Risk Principle --
5. Variable Significance Testing: a Statistical Approach --
6. Model Adequacy Testing --
7. Neural Networks in Tactical Asset Allocation: a Case Study --
8. Conclusions --
App. A. Computation of Network Derivatives --
App. B. Generating Random Normal Deviates.
Series Title: Perspectives in neural computing.
Responsibility: Achilleas Zapranis and Apostolos-Paul Refenes.
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