Toward a More Robust Variance-based Global Sensitivity Analysis of Model Outputs.

Tong, C.

Technical Information Center Oak Ridge Tennessee

关键词: Sensitivity analysis;    Variations;    Global;    Monte Carlo method;    Simulation;   

RP-ID  :  PB2012100592

学科分类：工程和技术（综合）

美国|英语

来源: National Technical Reports Library

【 摘 要 】

Global sensitivity analysis (GSA) measures the variation of a model output as a function of the variations of the model inputs given their ranges. In this paper we consider variance-based GSA methods that do not rely on certain assumptions about the model structure such as linearity or monotonicity. These variance-based methods decompose the output variance into terms of increasing dimensionality called 'sensitivity indices', first introduced by Sobol'. Sobol' developed a method of estimating these sensitivity indices using Monte Carlo simulations. McKay proposed an efficient method using replicated Latin hypercube sampling to compute the 'correlation ratios' or 'main effects', which have been shown to be equivalent to Sobol's first-order sensitivity indices. Practical issues with using these variance estimators are how to choose adequate sample sizes and how to assess the accuracy of the results. This paper proposes a modified McKay main effect method featuring an adaptive procedure for accuracy assessment and improvement. We also extend our adaptive technique to the computation of second-order sensitivity indices. Details of the proposed adaptive procedure as wells as numerical results are included in this paper.