论文标题
VAR比Sobol的指数更直观和高效吗？
Is VARS more intuitive and efficient than Sobol' indices?
论文作者
论文摘要
Razavi和Gupta已提出了响应表面（VAR）的变异函数分析，作为灵敏度分析的新综合框架。根据这些作者的说法，VAR提供了更直观的灵敏度概念，它比Sobol的指数更有效地计算效率。在这里，我们回顾了这些论点，并严格地将VARS-to的性能，对于总订单索引与总订单Jansen估计器进行比较。我们认为，与基于经典的方法不同，VAR缺乏对“重要”因素的明确定义，并表明所谓的VAR的计算优势无法承受审查。我们得出的结论是，虽然VAR富含现有方法的敏感性分析的频谱，尤其是用于诊断数学模型的诊断使用，但它补充了基于方差的灵敏度分析中使用的经典估计量。
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.
