Variance-based interaction index measuring heteroscedasticity
This work is motivated by the need to deal with models with high-dimensional input spaces of real variables. One way to tackle high-dimensional problems is to identify interaction or non-interaction among input parameters. We propose a new variance-based sensitivity interaction index that can detect...
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Veröffentlicht in: | Computer physics communications 2016-06, Vol.203, p.152-161 |
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creator | Ito, Keiichi Couckuyt, Ivo Poles, Silvia Dhaene, Tom |
description | This work is motivated by the need to deal with models with high-dimensional input spaces of real variables. One way to tackle high-dimensional problems is to identify interaction or non-interaction among input parameters. We propose a new variance-based sensitivity interaction index that can detect and quantify interactions among the input variables of mathematical functions and computer simulations. The computation is very similar to first-order sensitivity indices by Sobol’. The proposed interaction index can quantify the relative importance of input variables in interaction. Furthermore, detection of non-interaction for screening can be done with as low as 4n+2 function evaluations, where n is the number of input variables. Using the interaction indices based on heteroscedasticity, the original function may be decomposed into a set of lower dimensional functions which may then be analyzed separately. |
doi_str_mv | 10.1016/j.cpc.2016.02.032 |
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subjects | Computation Computer simulation Decomposition Dimensional analysis Interaction Mathematical analysis Mathematical models Optimization Parameter estimation Problem decomposition Real variables Screening Sensitivity analysis Sobol’ indices |
title | Variance-based interaction index measuring heteroscedasticity |
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