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Sensitivity analysis practices: Strategies for model-based inference
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DOI：10.1016/j.ress.2005.11.014.png)
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Abstract
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Fourteen years after Science's review of sensitivity analysis (SA) methods in 1989 (System analysis at molecular scale, by H. Rabitz) we search Science Online to identify and then review all recent articles having sensitivity analysis as a keyword. In spite of the considerable developments which have taken place in this discipline, of the good practices which have emerged, and of existing guidelines for SA issued on both sides of the Atlantic, we could not find in our review other than very primitive SA tools, based on one-factor-at-a-time (OAT) approaches. In the context of model corroboration or falsification, we demonstrate that this use of OAT methods is illicit and unjustified, unless the model under analysis is proved to be linear. We show that available good practices, such as variance based measures and others, are able to overcome OAT shortcomings and easy to implement. These methods also allow the concept of factors importance to be defined rigorously, thus making the factors importance ranking univocal. We analyse the requirements of SA in the context of modelling, and present best available practices on the basis of an elementary model. We also point the reader to available recipes for a rigorous SA. (c) 2005 Elsevier Ltd. All rights reserved.
Keywords:
global sensitivity analysis
Morris method
variance based methods
Monte Carlo filtering
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