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

被引：9

作者：

Wang, Xiaodi ^{[1
]}

Tang, Yincai ^{[1
]}

Zhang, Yingshan ^{[1
]}

机构：

[1] E China Normal Univ, Sch Finance & Stat, Shanghai 200241, Peoples R China

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2012年 / 142卷 / 07期

关键词：

Orthogonal array; Global sensitivity indices; A-optimality criterion; DESIGNS; OUTPUT;

D O I：

10.1016/j.jspi.2012.02.043

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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 Theta(M), the definition of which can be seen in Section 1. 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. (C) 2012 Elsevier B.V. All rights reserved.

引用

页码：1801 / 1810

页数：10

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