Controlled stratification for quantile estimation

By Claire Cannamela, Josselin Garnier, Bertrand Iooss. 2008

90 citations recorded by the source corpus.

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).

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/312a3cb6f5f480f5e0b389ff0aa7cee0e552be82

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

1. Pierre Etoré
2. Benjamin Jourdain
3. Bruno Sudret
4. V. Dubourg
5. François Deheeger
6. Joel Veness
7. Shane Legg

In Statistics and Probability

1. V. Dubourg
2. Bruno Sudret
3. François Deheeger
4. Anthony Nouy
5. Xiaoping Du
6. Zhen Hu
7. Géraud Blatman
8. Stefano Marelli

Citations in the corpus

• Metamodel-based importance sampling for structural reliability analysis (2011, methodology): “Cannamela et al. (2008) use a kriging prediction of the performance function g in order to build an instrumental PDF suited for the estimation of extreme quantiles of the random variate g(X).”
• A Review on Global Sensitivity Analysis Methods (2014, unknown): “[12]).”
• Adaptive Optimal Allocation in Stratified Sampling Methods (2007, background): “Another stratified sampling algorithm in which the optimal proportions and the conditional expectations are estimated using the same drawings has been very recently proposed in Cannamela et al. (2008) for quantile estimation.”