Evidence Optimization for Consequently Generated Models

Date

2010-12-04

Author

Strijov, Vadim
Krymova, Katya
Weber, Gerhard Wilhelm

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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To construct as adequate regression model one has to fulfill the set of measured features with their generated derivatives. Often the number of these features exceeds the number of the samples in the data set. After a feature generation process the problem of feature selection from a set of highly correlated features arises. The proposed algorithm uses an evidence maximization procedure to select a model as a subset of generated features. During the selection process it rejects multicollinear features. A problem of European option volatility modelling illustrates the algorithm. Its performance is compared with performance of similar well-known algorithms.

Subject Keywords

European option volatility, Multicollinearity, Model selection, Model evidence, Feature generation

URI

https://hdl.handle.net/11511/54143

DOI

https://doi.org/10.1063/1.3592467

Collections

Graduate School of Applied Mathematics, Conference / Seminar

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Citation Formats

V. Strijov, K. Krymova, and G. W. Weber, “Evidence Optimization for Consequently Generated Models,” 2010, vol. 1337, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54143.