Abstract
Global sensitivity analysis (GSA) is a useful tool to evaluate the influence of input variables in the whole distribution range. Variance-based methods and moment-independent methods are widely studied and popular GSA techniques despite their several shortcomings. Since probability weighted moments (PWMs) include more information than classical moments and can be accurately estimated from small samples, a novel global sensitivity measure based on PWMs is proposed. Then, two methods are introduced to estimate the proposed measure, i.e., double-loop-repeated-set numerical estimation and double-loop-single-set numerical estimation. Several numerical and engineering examples are used to show its advantages.
Highlights
Global Sensitivity Measure Based on Probability Weighted Moments
We introduce a novel sensitivity measure based on probability weighted moments (PWMs)
We propose a new global sensitivity measure based on PWMs
Summary
Global Sensitivity Measure Based on Probability Weighted Moments. To include the whole information of output distribution, moment-independent global sensitivity measures were proposed [15]. In this regard, Chun’s method preliminarily needs some assumptions [15]. We introduce a novel sensitivity measure based on probability weighted moments (PWMs). PWMs are fairly insensitive to outliers because they are linear combinations of samples [25] They can serve as constraints of the maximum entropy method to describe the distribution feature of the output, which are similar to classical moments [26].
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