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

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  • Luo, Xiaopeng
  • Lu, Zhenzhou
  • Xu, Xin

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.

Suggested Citation

  • Luo, Xiaopeng & Lu, Zhenzhou & Xu, Xin, 2014. "Non-parametric kernel estimation for the ANOVA decomposition and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 140-148.
    • Handle: RePEc:eee:reensy:v:130:y:2014:i:c:p:140-148
    DOI: 10.1016/j.ress.2014.06.002
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    Cited by:

    1. Mathpati, Yogesh Chandrakant & More, Kalpesh Sanjay & Tripura, Tapas & Nayek, Rajdip & Chakraborty, Souvik, 2023. "MAntRA: A framework for model agnostic reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Gámiz, Maria Luz & Lindqvist, Bo Henry, 2016. "Nonparametric estimation in trend-renewal processes," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 38-46.
    3. Senga Kiessé, Tristan & Ventura, Anne, 2016. "Discrete non-parametric kernel estimation for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 47-54.

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