A kernel estimate method for characteristic function-based uncertainty importance measure

作者：Xu, Xin; Lu, Zhenzhou^*; Luo, Xiaopeng

来源：Applied Mathematical Modelling, 2017, 42: 58-70.

DOI：10.1016/j.apm.2016.09.028

• Uncertainty importance analysis
• Moment-independent
• Characteristic function
• Kernel estimate method

In this paper, we propose a fast computation method based on a kernel function for the characteristic function-based moment-independent uncertainty importance measure theta(i). We first point out that the possible computational complexity problems that exist in the estimation of theta(i). 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 theta(i) 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 theta(i) and during the whole process, all theta(i) 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 theta(i).

• 出版日期2017-2
• 单位西北工业大学