  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).
