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Article Dans Une Revue Journal of Multivariate Analysis Année : 2013

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

Résumé

Given a reproducing kernel Hilbert space H of real-valued functions and a suitable measure mu over the source space D (subset of R), we decompose H as the sum of a subspace of centered functions for mu and its orthogonal in H. 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 e ffect of each (group of) variable(s) and computing sensitivity indices without recursivity.
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Dates et versions

hal-00601472 , version 1 (17-06-2011)
hal-00601472 , version 2 (07-12-2012)

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Nicolas Durrande, David Ginsbourger, Olivier Roustant, Laurent Carraro. ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis. Journal of Multivariate Analysis, 2013, 115, pp.57-67. ⟨hal-00601472v2⟩
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