Screening for Dispersion Effects by Sequential Bifurcation
Screening for Dispersion Effects by Sequential Bifurcation
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DOI：
10.1145/2651364
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
Bruce E. Ankenman;R. Cheng;S. Lewis
中科院分区：
文献类型：
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作者：
Bruce E. Ankenman;R. Cheng;S. Lewis
从系统或过程的计算机仿真模型的运行中获得的感兴趣的输出的平均值通常取决于许多因素;然而，许多时候，这些因素中只有少数是重要的。序列分叉是一种方法，已被认为是由几个作者确定这些重要的因素，使用尽可能少的运行的仿真模型。在这篇文章中，我们提出了一个新的顺序分岔过程，其步骤使用一个关键的停止规则，可以明确地计算，在以前考虑的最好的方法是不可用的。此外，我们展示了如何也可以很容易地修改这个停止规则，以有效地识别那些重要的因素，影响的变化，而不是平均输出。在实证研究中，新的方法比以前发表的完全序列分叉方法在实现规定的I型错误。它还实现了更高的功率检测适度大的影响，使用更少的复制比以前的方法。为了实现对中音效果的这种控制，在有许多非常大的效果的情况下，新方法有时需要比其他方法更多的复制。
The mean of the output of interest obtained from a run of a computer simulation model of a system or process often depends on many factors; many times, however, only a few of these factors are important. Sequential bifurcation is a method that has been considered by several authors for identifying these important factors using as few runs of the simulation model as possible. In this article, we propose a new sequential bifurcation procedure whose steps use a key stopping rule that can be calculated explicitly, something not available in the best methods previously considered. Moreover, we show how this stopping rule can also be easily modified to efficiently identify those factors that are important in influencing the variability rather than the mean of the output. In empirical studies, the new method performs better than previously published fully sequential bifurcation methods in terms of achieving the prescribed Type I error. It also achieves higher power for detecting moderately large effects using fewer replications than earlier methods. To achieve this control for midrange effects, the new method sometimes requires more replications than other methods in the case where there are many very large effects.