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Principles of Neural Model Identification, Selection and Adequacy : With Applications to Financial Econometrics /

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Neural networks have had considerable success in a variety of disciplines including engineering, control, and financial modelling. However a major weakness is the lack of established procedures for testing mis-specified models and the statistical significance of the various parameters which have bee...

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Bibliographic Details
Main Author: Zapranis, Achilleas
Corporate Author: SpringerLink (Online service)
Other Authors: Refenes, Apostolos-Paul N.
Format: eBook
Language:English
Published: London : Springer London, 1999.
Series:Perspectives in neural computing.
Subjects:
Computer science.
Artificial intelligence.
Economics > Statistics.
Economics.
Electronic books.
Statistics.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Introduction
  • Neural Model Identification
  • Review of Current Practice in Neural Model Identification
  • Neural Model Selection: Minimum Prediction Risk Principle
  • Variable Significance Testing
  • Model Adequacy Testing
  • Tactical Asset Allocation
  • Implied Volatility Forecasting for Options Pricing with Neural Nets
  • Appendix 1: Computing Network Derivatives
  • Appendix 2: Generating Random Deviates
  • Bibliography.