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

详细信息    查看全文

• 作者：N. Durrande^a ; ^b ; ^{durrande@gmail.com} ; ^{durrande@emse.fr} ; D. Ginsbourger^c ; ^{ginsbourger@gmail.com} ; O. Roustant^b ; ^{roustant@emse.fr} ; L. Carraro^d ; ^{laurent.carraro@telecom-st-etienne.fr}
• 关键词：49Q12 ; 46N30 ; 60G15 ; 62J05 ; 62J10
• 刊名：Journal of Multivariate Analysis
• 出版年：2013
• 出版时间：March, 2013
• 年：2013
• 卷：115
• 期：Complete
• 页码：57-67
• 全文大小：378 K

文摘

Given a reproducing kernel Hilbert space of real-valued functions and a suitable measure over the source space , we decompose as the sum of a subspace of centered functions for and its orthogonal in . 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.