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Full Record Details
Persistent URL
http://purl.org/net/epubs/work/34304
Record Status
Checked
Record Id
34304
Title
Confidence in Data Mining Model Predictions:a Financial Engineering Application
Contributors
JV Healy (London Metropolitan University)
,
M Dixon (London Metropolitan University)
,
BJ Read (London Metropolitan University)
,
FF Cai (London Metropolitan University)
Abstract
This paper describes a generally applicable robust method for determining prediction intervals for models derived by non-linear regression. Hypothesis tests for bias are applied. The concept is demonstrated by application to a standard synthetic example, and is then applied to prediction intervals for a financial engineering example viz. option pricing using data from LIFFE for 'ESX' European style options on the FTSE 100 index. Unbiased estimates of the standard error are obtained. The method uses standard regression procedures to determine local error bars and avoids programming special architectures. It is appropriate for target data with non-constant variance.
Organisation
CCLRC
,
BITD
Keywords
Data Mining Neural Nets
,
Decision Support
Funding Information
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Language
English (EN)
Type
Details
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Year
Paper In Conference Proceedings
Refereed Workshop Proceedings on CD . Is in proceedings of: Intelligent Systems Workshop.
IECON03P1331.pdf
2003
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