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Discrete non-parametric kernel estimation for global sensitivity analysis
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DOI：10.1016/j.ress.2015.10.010.png)
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Abstract
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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 nonparametric 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. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords:
Analysis of variance
Discrete kernel
Non-parametric regression
Sensitivity analysis
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