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

Xiaopeng Luo, Zhenzhou Lu and Xin Xu

Reliability Engineering and System Safety, 2014, vol. 130, issue C, 140-148

Abstract: 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.

Keywords: ANOVA decomposition; Sensitivity analysis (SA); Non-parametric methods; Kernel estimate; Higher-order kernels; Conditional moments (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:130:y:2014:i:c:p:140-148

DOI: 10.1016/j.ress.2014.06.002

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