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Traditional Global Sensitivity Analysis(GSA) focuses on ranking inputs according to their contributions to the output uncertainty.However,information about how the specific regions inside an input affect the output is beyond the traditional GSA techniques.To fully address this issue,in this work,two regional moment-independent importance measures,Regional Importance Measure based on Probability Density Function(RIMPDF) and Regional Importance Measure based on Cumulative Distribution Function(RIMCDF),are introduced to find out the contributions of specific regions of an input to the whole output distribution.The two regional importance measures prove to be reasonable supplements of the traditional GSA techniques.The ideas of RIMPDF and RIMCDF are applied in two engineering examples to demonstrate that the regional moment-independent importance analysis can add more information concerning the contributions of model inputs.
Traditional Global Sensitivity Analysis (GSA) focuses on ranking inputs according to their contributions to the output uncertainty. However, about about the specific regions inside an input affect the output is beyond the traditional GSA techniques.To fully address this issue, in this work , two regional moment-independent importance measures, Regional Importance Measure based on Probability Density Function (RIMPDF) and Regional Importance Measure based on Cumulative Distribution Function (RIMCDF), are introduced to find out the contributions of specific regions of an input to the whole output distribution. The two regional importance measures prove to be reasonable supplements of the traditional GSA techniques. The ideas of RIMPDF and RIMCDF are applied in two engineering examples to demonstrate that the regional moment-independent importance of analysis can add more information concerning the contributions of model inputs .