Importance analysis for models with correlated variables and its sparse grid solution

Luyi Li and Zhenzhou Lu

Reliability Engineering and System Safety, 2013, vol. 119, issue C, 207-217

Abstract: For structural models involving correlated input variables, a novel interpretation for variance-based importance measures is proposed based on the contribution of the correlated input variables to the variance of the model output. After the novel interpretation of the variance-based importance measures is compared with the existing ones, two solutions of the variance-based importance measures of the correlated input variables are built on the sparse grid numerical integration (SGI): double-loop nested sparse grid integration (DSGI) method and single loop sparse grid integration (SSGI) method. The DSGI method solves the importance measure by decreasing the dimensionality of the input variables procedurally, while SSGI method performs importance analysis through extending the dimensionality of the inputs. Both of them can make full use of the advantages of the SGI, and are well tailored for different situations. By analyzing the results of several numerical and engineering examples, it is found that the novel proposed interpretation about the importance measures of the correlated input variables is reasonable, and the proposed methods for solving importance measures are efficient and accurate.

Keywords: Correlated input variables; Variance-based importance measure; Sparse grid integration; Single loop Monte Carlo method (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:119:y:2013:i:c:p:207-217

DOI: 10.1016/j.ress.2013.06.036

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