Statistical inference for Sobol pick-freeze Monte Carlo method
By Fabrice Gamboa, Alexandre Janon, Thierry Klein, Agnès Lagnoux-Renaudie, Clémentine Prieur. 2013
82 citations recorded by the source corpus.
Many mathematical models involve input parameters, which are not precisely known. Global sensitivity analysis aims to identify the parameters whose uncertainty has the largest impact on the variability of a quantity of interest (output of the model). One of the statistical tools used to quantify the influence of each input variable on the output is the Sobol sensitivity index. We consider the statistical estimation of this index from a finite sample of model outputs. We study asymptotic and non-asymptotic properties of two estimators of Sobol indices. These properties are applied to significance tests and estimation by confidence intervals.
Subfields: Computational Theory and Mathematics, Mechanics of Materials, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/6bd61e65c741df5648d868078303b1a55d7c7f6c
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In Computational Theory and Mathematics
In Statistics and Probability
Citations in the corpus
- The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “Their emphasis has particularly been placed on linking SA and asymptotic statistical theory (Janon et al., 2014a; Gamboa et al., 2016), space-filling designs of experiments (Tissot and Prieur, 2015; Gilquin et al., 2020), structural reliability (Fort et al., 2016), and Bayesian estimation…”
- Global Sensitivity Analysis: a new generation of mighty estimators based on rank statistics (2020, unknown): “In addition, the sequence of estimators (T 1 n)n is asymptotically efficient to estimate S1 from such a design of experiment (see, [37] for the definition of the asymptotic efficiency and [13] for the details of the result).”
- Sensitivity analysis for multidimensional and functional outputs (2013, background): “17 of Ledoux [8] leads to bounds improving the one obtained in [5].”