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Discrete non-parametric kernel estimation for global sensitivity analysis

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  • Senga Kiessé, Tristan
  • Ventura, Anne

Abstract

This work investigates the discrete kernel approach for evaluating the contribution of the variance of discrete input variables to the variance of model output, via analysis of variance (ANOVA) decomposition. Until recently only the continuous kernel approach has been applied as a metamodeling approach within sensitivity analysis framework, for both discrete and continuous input variables. Now the discrete kernel estimation is known to be suitable for smoothing discrete functions. We present a discrete non-parametric kernel estimator of ANOVA decomposition of a given model. An estimator of sensitivity indices is also presented with its asymtotic convergence rate. Some simulations on a test function analysis and a real case study from agricultural have shown that the discrete kernel approach outperforms the continuous kernel one for evaluating the contribution of moderate or most influential discrete parameters to the model output.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:146:y:2016:i:c:p:47-54
    DOI: 10.1016/j.ress.2015.10.010
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    References listed on IDEAS

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    1. Rechard, Rob P. & Liu, Hui-Hai & Tsang, Yvonne W. & Finsterle, Stefan, 2014. "Site characterization of the Yucca Mountain disposal system for spent nuclear fuel and high-level radioactive waste," Reliability Engineering and System Safety, Elsevier, vol. 122(C), pages 32-52.
    2. Kokonendji, Célestin C. & Zocchi, Silvio S., 2010. "Extensions of discrete triangular distributions and boundary bias in kernel estimation for discrete functions," Statistics & Probability Letters, Elsevier, vol. 80(21-22), pages 1655-1662, November.
    3. 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.
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