Monte Carlo simulation for moment-independent sensitivity analysis

Pengfei Wei, Zhenzhou Lu and Xiukai Yuan

Reliability Engineering and System Safety, 2013, vol. 110, issue C, 60-67

Abstract: The moment-independent sensitivity analysis (SA) is one of the most popular SA techniques. It aims at measuring the contribution of input variable(s) to the probability density function (PDF) of model output. However, compared with the variance-based one, robust and efficient methods are less available for computing the moment-independent SA indices (also called delta indices). In this paper, the Monte Carlo simulation (MCS) methods for moment-independent SA are investigated. A double-loop MCS method, which has the advantages of high accuracy and easy programming, is firstly developed. Then, to reduce the computational cost, a single-loop MCS method is proposed. The later method has several advantages. First, only a set of samples is needed for computing all the indices, thus it can overcome the problem of “curse of dimensionality†. Second, it is suitable for problems with dependent inputs. Third, it is purely based on model output evaluation and density estimation, thus can be used for model with high order (>2) interactions. At last, several numerical examples are introduced to demonstrate the advantages of the proposed methods.

Keywords: Monte Carlo simulation; Moment-independent sensitivity analysis; Delta indices; Kernel density estimation (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:110:y:2013:i:c:p:60-67

DOI: 10.1016/j.ress.2012.09.005

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