Skip to Main content Skip to Navigation
Journal articles

Controlled stratification for quantile estimation

Abstract : In this paper we propose and discuss variance reduction techniques for the estimation of quantiles of the output of a complex model with random input parameters. These techniques are based on the use of a reduced model, such as a metamodel or a response surface. The reduced model can be used as a control variate; or a rejection method can be implemented to sample the realizations of the input parameters in prescribed relevant strata; or the reduced model can be used to determine a good biased distribution of the input parameters for the implementation of an importance sampling strategy. The different strategies are analyzed and the asymptotic variances are computed, which shows the benefit of an adaptive controlled stratification method. This method is finally applied to a real example (computation of the peak cladding temperature during a large-break loss of coolant accident in a nuclear reactor).
Document type :
Journal articles
Complete list of metadata

Cited literature [33 references]  Display  Hide  Download
Contributor : Bibliothèque CADARACHE Connect in order to contact the contributor
Submitted on : Monday, March 23, 2020 - 9:52:59 AM
Last modification on : Thursday, May 21, 2020 - 1:37:51 AM
Long-term archiving on: : Wednesday, June 24, 2020 - 1:32:33 PM


Publisher files allowed on an open archive





Claire Cannamela, Josselin Garnier, Bertrand Iooss. Controlled stratification for quantile estimation. Annals of Applied Statistics, Institute of Mathematical Statistics, 2008, 2 (4), pp.1554 - 1580. ⟨10.1214/08-AOAS186⟩. ⟨cea-02514913⟩



Record views


Files downloads