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- W2531426656 abstract "In this paper, we propose a fast computation method based on a kernel function for the characteristic function-based moment-independent uncertainty importance measure θi. We first point out that the possible computational complexity problems that exist in the estimation of θi. Since the convergence rate of a double-loop Monte Carlo (MC) simulation is O(N−1/4), the first possible problem is the use of double-loop MC simulation. And because the norm of the difference between the unconditional and conditional characteristic function of model output in θi is a Lebesgue integral over the infinite interval, another possible problem is the computation of this norm. Then a kernel function is introduced to avoid the use of double-loop MC simulation, and a longer enough bounded interval is selected to instead of the infinite interval to calculate the norm. According to these improvements, a kind of fast computational methods is introduced for θi, and during the whole process, all θi can be obtained by using a single quasi-MC sequence. From the comparison of numerical error analysis, it can be shown that the proposed method is an effective and helpful approach for computing the characteristic function-based moment-independent importance index θi." @default.
- W2531426656 created "2016-10-21" @default.
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- W2531426656 date "2017-02-01" @default.
- W2531426656 modified "2023-09-23" @default.
- W2531426656 title "A kernel estimate method for characteristic function-based uncertainty importance measure" @default.
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- W2531426656 doi "https://doi.org/10.1016/j.apm.2016.09.028" @default.
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