Global sensitivity analysis of stochastic computer models with joint metamodels

Abstract : The global sensitivity analysis method used to quantify the influence of uncertain input variables on the variability in numerical model responses has already been applied to deterministic computer codes; deterministic means here that the same set of input variables gives always the same output value. This paper proposes a global sensitivity analysis methodology for stochastic computer codes, for which the result of each code run is itself random. The framework of the joint modeling of the mean and dispersion of heteroscedastic data is used. To deal with the complexity of computer experiment outputs, nonparametric joint models are discussed and a new Gaussian process-based joint model is proposed. The relevance of these models is analyzed based upon two case studies. Results show that the joint modeling approach yields accurate sensitivity index estimatiors even when heteroscedasticity is strong.
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Soumis le : lundi 23 mai 2011 - 16:45:45
Dernière modification le : vendredi 10 janvier 2020 - 21:08:59
Archivage à long terme le : mercredi 24 août 2011 - 02:26:39


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  • HAL Id : hal-00525489, version 2


Amandine Marrel, Bertrand Iooss, Sébastien da Veiga, Mathieu Ribatet. Global sensitivity analysis of stochastic computer models with joint metamodels. Statistics and Computing, Springer Verlag (Germany), 2012, 22, pp.833-847. ⟨hal-00525489v2⟩



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