Articlebronze2008

Controlled stratification for quantile estimation

68 citations0/0 files storedThe Annals of Applied Statistics

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Probabilistic and Robust Engineering Design Simulation Techniques and Applications Markov Chains and Monte Carlo Methods

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).

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References

1989Design and Analysis of Computer Experiments 1981Simulation and the Monte Carlo Method 2005Design and Modeling for Computer Experiments 1993A New Class of Supersaturated Designs 2008Sequential Experiment Design for Contour Estimation From Complex Computer Codes 2003Evaluation of nuclear safety from the outputs of computer codes in the presence of uncertainties 2004Estimating Percentiles of Uncertain Computer Code Outputs

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