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 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 we 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 replaces classic estimators used in variance-based sensitivity analysis.

Razavi和Gupta提出了响应面的方差分析（VARS），作为灵敏度分析的新的综合框架。这些作者认为，VARS提供了更直观的敏感性概念，并且比Sobol指数具有更高的计算效率。在这里，我们回顾这些论点，并严格比较VARS-TO（针对总订单指数）的性能与总订单Jansen估计量。我们认为，与传统的基于方差的方法不同，VARS缺乏对“重要”因素是什么的明确定义，并且我们证明了VARS的所谓的计算优势无法经受审查。我们得出的结论是，虽然VARS丰富了用于敏感性分析的现有方法的范围，尤其是用于数学模型的诊断，