论文标题
全球灵敏度分析的总阶估计器的全面比较
A comprehensive comparison of total-order estimators for global sensitivity analysis
论文作者
论文摘要
灵敏度分析有助于确定哪些模型输入传达了模型输出的最不确定性。全球灵敏度分析中最有权威的措施之一是SOBOL的总级指数，可以用几个不同的估计器计算。尽管存在先前的比较，但很难知道哪种估计器的性能最佳，因为结果取决于分析师定义的基准设置（采样方法，模型输入的分布，模型运行的数量，测试功能或模型及其维度，其维度及其尺寸，更高级别的效果的重量或选择的性能测量值或选择的性能测量值）。在这里，我们比较了八维超立方体中的几个总阶估计器，其中这些基准参数被视为随机参数。这种布置显着放松了结果对基准设计的依赖性。我们观察到，最准确的估计量是Razavi和Gupta，Jansen或Janon/Monod的因子优先次序，以及Jansen's，Janon/Monod's或Azzini和Rosati和Rosati接近“真实”总订单内置。其余的落后于落后。我们的工作通过减少选择最合适的估计量的不确定性来帮助分析师浏览众多总订单公式。
Sensitivity analysis helps identify which model inputs convey the most uncertainty to the model output. One of the most authoritative measures in global sensitivity analysis is the Sobol' total-order index, which can be computed with several different estimators. Although previous comparisons exist, it is hard to know which estimator performs best since the results are contingent on the benchmark setting defined by the analyst (the sampling method, the distribution of the model inputs, the number of model runs, the test function or model and its dimensionality, the weight of higher order effects or the performance measure selected). Here we compare several total-order estimators in an eight-dimension hypercube where these benchmark parameters are treated as random parameters. This arrangement significantly relaxes the dependency of the results on the benchmark design. We observe that the most accurate estimators are Razavi and Gupta's, Jansen's or Janon/Monod's for factor prioritization, and Jansen's, Janon/Monod's or Azzini and Rosati's for approaching the "true" total-order indices. The rest lag considerably behind. Our work helps analysts navigate the myriad of total-order formulae by reducing the uncertainty in the selection of the most appropriate estimator.
