用于分位数估计的受控分层
Controlled stratification for quantile estimation
摘要：
在本文中，我们提出并讨论方差减少技术的估计分位数的输出与随机输入参数的复杂模型。这些技术基于使用简化模型，例如元模型或响应曲面。简化模型可以用作控制变量;或者可以实施拒绝方法以在规定的相关层中对输入参数的实现进行采样;或者简化模型可以用于确定输入参数的良好的有偏分布以用于实施重要性抽样策略。不同的策略进行了分析和渐近方差的计算，这表明自适应控制分层方法的好处。最后将该方法应用于一个实例（核反应堆大破口失水事故包壳峰值温度的计算）。
英文摘要：
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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