Variance-Based Sensitivity Analysis: Theory and Estimation Algorithms

Clémentine Prieur () and Stefano Tarantola ()
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Clémentine Prieur: University of Grenoble Alpes, INRIA, Laboratoire Jean Kuntzmann (LJK)
Stefano Tarantola: Joint Research Centre of the European Commission, Statistical Indicators for Policy Assessment

Chapter 35 in Handbook of Uncertainty Quantification, 2017, pp 1217-1239 from Springer

Abstract: Abstract This section aims at presenting an overview of variance-based approaches for global sensitivity analysis. Starting from functional ANOVA, Sobol’ indices are first defined and then estimation algorithms are provided. The performance of these algorithms is theorically and practically discussed. The review includes recent results on the topic.

Keywords: FANOVA; Sobol’ sensitivity indices; Global sensitivity analysis; Monte Carlo sampling; Quasi-Monte Carlo sampling; Sampling design; Replication; Latin hypercube sampling; Orthogonal arrays (OA); Spectral methods; Fourier amplitude sensitivity test; Random balance design; Effective algorithm for sensitivity indices; Polynomial chaos expansion; effective dimension; Sensitivity indices with given data (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-319-12385-1_35

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