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Sensitivity analysis (SA) of model output investigates the relationship between the predictions of a model, possibly implemented in a computer program, and its input parameters. Such an analysis is relevant for a number of practices, including quality assurance of models and codes, and the identification of crucial regions in the parameter space. This note compares established techniques with variants, such as a modified version of the Hora and Iman importance measure, or new methods, such as the iterated fractional factorial design. Comparison is made on the basis of method reproducibility and of method accuracy. International benchmark test models were taken from the field of performance analysis of nuclear waste disposal in geological formations. The results based on these models show that the modified version of the Hora and Iman method proposed in this paper is extremely robust, when compared with the other existing statistics, even in the presence of model non-monotonicities.

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Authors: SALTELLI A, JRC Ispra (IT);ANDRES T H, Atomic Energy of Canada Ltd., Pinawa Man (CA);HOMMA T, JAERI, Naka-gun, Ibaraki-ken (JP)
Bibliographic Reference: Article: Computational Statistics & Data Analysis, Vol. 15 (1993) pp. 211-238
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