Metamodel construction for sensitivity analysis

Sylvie Huet (1) , Marie-Luce Taupin (2, 1)

1 MaIAGE - Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas]
2 LaMME - Laboratoire de Mathématiques et Modélisation d'Evry

We propose to estimate a metamodel and the sensitivity indices of a complex model m in the Gaussian regression framework. Our approach combines methods for sensitivity analysis of complex models and statistical tools for sparse non-parametric estimation in multivariate Gaussian regression model. It rests on the construction of a metamodel for aproximating the Hoeffding-Sobol decomposition of m. This metamodel belongs to a reproducing kernel Hilbert space constructed as a direct sum of Hilbert spaces leading to a functional ANOVA decomposition. The estimation of the metamodel is carried out via a penalized least-squares minimization allowing to select the subsets of variables that contribute to predict the output. It allows to estimate the sensitivity indices of m. We establish an oracle-type inequality for the risk of the estimator, describe the procedure for estimating the metamodel and the sensitivity indices, and assess the performances of the procedure via a simulation study.

Domaines: Statistiques [math.ST]

Sylvie Huet, Marie-Luce Taupin. Metamodel construction for sensitivity analysis. ESAIM: Proceedings, 2017, Journées MAS 2016 de la SMAI – Phénomènes complexes et hétérogènes, Volume 60, 2017 (60), pp.27-69. ⟨hal-01434895v1⟩