Durrande, Nicolas; Ginsbourger, David; Roustant, Olivier; Carraro, Laurent (2013). ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis. Journal of multivariate analysis, 115, pp. 57-67. Elsevier 10.1016/j.jmva.2012.08.016
Full text not available from this repository.Given a reproducing kernel Hilbert space (H,〈.,.〉)(H,〈.,.〉) of real-valued functions and a suitable measure μμ over the source space D⊂RD⊂R, we decompose HH as the sum of a subspace of centered functions for μμ and its orthogonal in HH. This decomposition leads to a special case of ANOVA kernels, for which the functional ANOVA representation of the best predictor can be elegantly derived, either in an interpolation or regularization framework. The proposed kernels appear to be particularly convenient for analyzing the effect of each (group of) variable(s) and computing sensitivity indices without recursivity.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
08 Faculty of Science > Department of Mathematics and Statistics > Institute of Mathematical Statistics and Actuarial Science |
UniBE Contributor: |
Ginsbourger, David |
Subjects: |
500 Science > 510 Mathematics |
ISSN: |
0047-259X |
Publisher: |
Elsevier |
Language: |
English |
Submitter: |
Lutz Dümbgen |
Date Deposited: |
01 Apr 2014 03:15 |
Last Modified: |
05 Dec 2022 14:28 |
Publisher DOI: |
10.1016/j.jmva.2012.08.016 |
URI: |
https://boris.unibe.ch/id/eprint/41509 |
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