Non-parametric kernel estimation for the ANOVA decomposition and sensitivity analysis

被引：13
|
作者：
Luo, Xiaopeng [1 ]
Lu, Zhenzhou [1 ]
Xu, Xin [1 ]
机构：
[1] Northwestern Polytech Univ, Sch Aeronaut, Xian 710072, Peoples R China
来源：
RELIABILITY ENGINEERING & SYSTEM SAFETY | 2014年 / 130卷
基金：
中国国家自然科学基金;
关键词：
ANOVA decomposition; Sensitivity analysis (SA); Non-parametric methods; Kernel estimate; Higher-order kernels; Conditional moments; RATES; CONVERGENCE;
D O I：
10.1016/j.ress.2014.06.002
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In this paper, we consider the non-parametric estimation of the analysis of variance (ANOVA) decomposition, which is useful for applications in sensitivity analysis (SA) and in the more general emulation framework. Pursuing the point of view of the state-dependent parameter (SDP) estimation, the non-parametric kernel estimation (including high order kernel estimator) is built for those purposes. On the basis of the kernel technique, the asymptotic convergence rate is theoretically obtained for the estimator of sensitivity indices. It is shown that the kernel estimation can provide a faster convergence rate than the SDP estimation for both the ANOVA decomposition and the sensitivity indices. This would help one to get a more accurate estimation at a smaller computational cost (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码：140 / 148
页数：9
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