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- W2088317792 abstract "Abstract This work focuses on the fast computation of the moment-independent importance measure δ i . We first analyse why δ i is associated with a possible computational complexity problem. One of the reasons that we thought of is the use of two-loop Monte Carlo simulation, because its rate of convergence is O ( N − 1 / 4 ) , and another one is the computation of the norm of the difference between a density and a conditional density. We find that these problems are nonessential difficulties and try to give associated improvements. A kernel estimate is introduced to avoid the use of two-loop Monte Carlo simulation, and a moment expansion of the associated norm which is not simply obtained by using the Edgeworth series is proposed to avoid the density estimation. Then, a fast computational method is introduced for δ i . In our method, all δ i can be obtained by using a single sample set. From the comparison of the numerical error analyses, we believe that the proposed method is clearly helpful for improving computational efficiency." @default.
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- W2088317792 date "2014-01-01" @default.
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- W2088317792 title "A fast computational method for moment-independent uncertainty importance measure" @default.
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- W2088317792 doi "https://doi.org/10.1016/j.cpc.2013.08.006" @default.
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