ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis

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

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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:

01 Apr 2014 03:16

Publisher DOI:

10.1016/j.jmva.2012.08.016

URI:

https://boris.unibe.ch/id/eprint/41509

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