A new computational method of a moment-independent uncertainty importance measure

Abstract

For a risk assessment model, the uncertainty in input parameters is propagated through the model and leads to the uncertainty in the model output. The study of how the uncertainty in the output of a model can be apportioned to the uncertainty in the model inputs is the job of sensitivity analysis. Saltelli [Sensitivity analysis for importance assessment. Risk Analysis 2002;22(3):579–90] pointed out that a good sensitivity indicator should be global, quantitative and model free. Borgonovo [A new uncertainty importance measure. Reliability Engineering and System Safety 2007;92(6):771–84] further extended these three requirements by adding the fourth feature, moment-independence, and proposed a new sensitivity measure, δ i . It evaluates the influence of the input uncertainty on the entire output distribution without reference to any specific moment of the model output. In this paper, a new computational method of δ i is proposed. It is conceptually simple and easier to implement. The feasibility of this new method is proved by applying it to two examples.