Orthogonal arrays for estimating global sensitivity indices of non-parametric models based on ANOVA high-dimensional model representation

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• 作者：Xiaodi Wang ; Yincai Tang ; ^{tangyc8866@gmail.com} ; Yingshan Zhang
• 关键词：Orthogonal array ; Global sensitivity indices ; A-optimality criterion
• 刊名：Journal of Statistical Planning and Inference
• 出版年：2012
• 期刊代码：36_03783758
• 类别：mt
• 出版时间：July, 2012
• 卷：142
• 期：7
• 页码：1801-1810
• 文件大小：189 K

摘要

Global sensitivity indices play important roles in global sensitivity analysis based on ANOVA high-dimensional model representation. However, few effective methods are available for the estimation of the indices when the objective function is a non-parametric model. In this paper, we explore the estimation of global sensitivity indices of non-parametric models. The main result (Theorem 2.1) shows that orthogonal arrays (OAs) are A-optimality designs for the estimation of the definition of which can be seen in . Estimators of global sensitivity indices are proposed based on orthogonal arrays and proved to be accurate for small indices. The performance of the estimators is illustrated by a simulation study.