Quickly Assessing Contributions to Input Uncertainty
Quickly Assessing Contributions to Input Uncertainty
复制标题
快速评估对输入不确定性的贡献
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
B. Nelson
中科院分区：
文献类型：
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作者：
Eunhye Song;B. Nelson
“输入不确定性”是指基于模拟的性能估计量中的（通常未测量的）变异性，这种变异性是使用基于现实世界数据的输入模型（例如，独立同分布输入的完全指定的单变量分布）驱动模拟的结果。2012年，安肯曼和尼尔森提出了一种快速简便的诊断实验，以评估输入不确定性对模拟输出的总体影响。当他们的方法表明输入不确定性很大时，接下来自然的问题是哪些输入分布对输入不确定性的贡献最大，以及从哪些输入分布收集更多数据最为有益？他们提出了一系列可能很长的附加诊断实验来回答这些问题。在本文中，我们提供了一种方法，该方法从单个诊断实验中获得由于输入不确定性导致的总体方差的估计量、每个输入分布对该方差的相对贡献，以及总体不确定性对用于拟合每个分布的现实世界样本量增加的敏感度的度量。我们的方法利用了一个元模型，该元模型将输入分布的均值和方差与模拟输出的平均响应相关联，并对现实世界数据进行自助抽样以表示输入模型的不确定性。此外，我们研究是否以及如何将名义实验和诊断实验的模拟输出相结合以获得更好的性能估计量。对于分析人员获得额外的现实世界数据、细化输入模型并进行后续实验的情况，我们分析是否以及如何将所有三个实验的模拟输出相结合。文中提供了数值示例。
“Input uncertainty” refers to the (often unmeasured) variability in simulation-based performance estimators that is a consequence of driving the simulation with input models (e.g., fully specified univariate distributions of i.i.d. inputs) that are based on real-world data. In 2012 Ankenman and Nelson presented a quick-and-easy diagnostic experiment to assess the overall effect of input uncertainty on simulation output. When their method reveals that input uncertainty is substantial, then the natural next questions are which input distributions contribute the most to input uncertainty, and from which input distributions would it be most beneficial to collect more data? They proposed a possibly lengthy sequence of additional diagnostic experiments to answer these questions. In this paper we provide a method that obtains an estimator of the overall variance due to input uncertainty, the relative contribution to this variance of each input distribution, and a measure of the sensitivity of overall uncertainty to increasing the real-world sample-size used to fit each distribution, all from a single diagnostic experiment. Our approach exploits a metamodel that relates the means and variances of the input distributions to the mean response of the simulation output, and bootstrapping of the real-world data to represent input-model uncertainty. Further, we investigate whether and how the simulation outputs from the nominal and diagnostic experiments may be combined to obtain a better performance estimator. For the case when the analyst obtains additional real-world data, refines the input models, and runs a follow-up experiment, we analyze whether and how the simulation outputs from all three experiments should be combined. Numerical illustrations are provided.