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

N. Durrande, D. Ginsbourger, O. Roustant and L. Carraro

Journal of Multivariate Analysis, 2013, vol. 115, issue C, 57-67

Abstract: Given a reproducing kernel Hilbert space (H,〈.,.〉) of real-valued functions and a suitable measure μ over the source space D⊂R, we decompose H as the sum of a subspace of centered functions for μ 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 effect of each (group of) variable(s) and computing sensitivity indices without recursivity.

Keywords: Gaussian process regression; Global sensitivity analysis; Hoeffding–Sobol decomposition; SS-ANOVA (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.jmva.2012.08.016

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