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• W1908411054 abstract "The error estimate of Borgonovo's moment‐independent index is considered, and it shows that the possible computational complexity of is mainly due to the probability density function (PDF) estimate because the PDF estimate is an ill‐posed problem and its convergence rate is quite slow. So it reminds us to compute Borgonovo's index using other methods. To avoid the PDF estimate, , which is based on the PDF, is first approximatively represented by the cumulative distribution function (CDF). The CDF estimate is well posed and its convergence rate is always faster than that of the PDF estimate. From the representation, a stable approach is proposed to compute with an adaptive procedure. Since the small probability multidimensional integral needs to be computed in this procedure, a computational strategy named asymptotic space integration is introduced to reduce a high‐dimensional integral to a one‐dimensional integral. Then we can compute the small probability multidimensional integral by adaptive numerical integration in one dimension with an improved convergence rate. From the comparison of numerical error analysis of some examples, it can be shown that the proposed method is an effective approach to uncertainty importance measure computation." @default.
• W1908411054 created "2016-06-24" @default.
• W1908411054 creator A5031417213 @default.
• W1908411054 creator A5075613482 @default.
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• W1908411054 date "2013-07-08" @default.
• W1908411054 modified "2023-10-03" @default.
• W1908411054 title "A Stable Approach Based on Asymptotic Space Integration for Moment-Independent Uncertainty Importance Measure" @default.
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• W1908411054 doi "https://doi.org/10.1111/risa.12087" @default.
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• W1908411054 hasPublicationYear "2013" @default.
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