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Persistent URL http://purl.org/net/epubs/work/34304
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Record Id 34304
Title Confidence in Data Mining Model Predictions:a Financial Engineering Application
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
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Language English (EN)
Type Details URI(s) Local file(s) Year
Paper In Conference Proceedings Refereed Workshop Proceedings on CD . Is in proceedings of: Intelligent Systems Workshop. IECON03P1331.pdf 2003