VARS是否比Sobol的索引更直观、更有效？
Is VARS more intuitive and efficient than Sobol' indices?
摘要：
响应面变差函数分析是Razavi和Gupta提出的一种新的敏感性分析综合框架。根据这些作者的说法，VAR提供了一个更直观的敏感性概念，并且比Sobol指数的计算效率高得多。在这里，我们回顾这些参数，并严格比较VARS-TO的性能，总阶指数，对总阶詹森估计。我们认为，与经典的方差为基础的方法，VAR缺乏一个明确的定义是什么“重要”的因素，并表明，所谓的计算优势的VAR不经得起审查。我们的结论是，虽然VAR丰富了现有的敏感性分析，特别是诊断使用的数学模型的方法的频谱，它补充，而不是替代经典的估计方差为基础的敏感性分析。
英文摘要：
The Variogram Analysis of Response Surfaces (VARS) has been proposed by Razavi and Gupta as a new comprehensive framework in sensitivity analysis. According to these authors, VARS provides a more intuitive notion of sensitivity and it is much more computationally efficient than Sobol' indices. Here we review these arguments and critically compare the performance of VARS-TO, for total-order index, against the total-order Jansen estimator. We argue that, unlike classic variance-based methods, VARS lacks a clear definition of what an "important" factor is, and show that the alleged computational superiority of VARS does not withstand scrutiny. We conclude that while VARS enriches the spectrum of existing methods for sensitivity analysis, especially for a diagnostic use of mathematical models, it complements rather than substitutes classic estimators used in variance-based sensitivity analysis.
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