Publication: High-order statistics in global sensitivity analysis: Decomposition and model reduction
High-order statistics in global sensitivity analysis: Decomposition and model reduction
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Geraci, G., Congedo, P. M., Abgrall, R., & Iaccarino, G. (2016). High-order statistics in global sensitivity analysis: Decomposition and model reduction. Computer Methods in Applied Mechanics and Engineering, 301, 80–115. https://doi.org/10.1016/j.cma.2015.12.022
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Abstract
Analysis of Variance (ANOVA) is a common technique for computing a ranking of the input parameters in terms of their contribution to the output variance. Nevertheless, the variance is not a universal criterion for ranking variables, since non symmetric outputs could require higher order statistics for their description and analysis. In this work, we illustrate how third and fourth-order moments, i.e. skewness and kurtosis, respectively, can be decomposed mimicking the ANOVA approach. It is also shown how this decomposition is correlat
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- Geraci, Gianluca
- Congedo, Pietro Marco
- Iaccarino, Gianluca
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Geraci, G., Congedo, P. M., Abgrall, R., & Iaccarino, G. (2016). High-order statistics in global sensitivity analysis: Decomposition and model reduction. Computer Methods in Applied Mechanics and Engineering, 301, 80–115. https://doi.org/10.1016/j.cma.2015.12.022
