Non-parametric statistics in sensitivity analysis for model output: A comparison of selected techniques

The use of Statistics in risk assessment studied is an expanding field where the selection of the proper technique is often difficult to make. This is the case with the sensitivity analysis methods used in conjunction with Monte Carlo computer codes. The Monte Carlo approach is commonly used in risk assessment, where it can be used to estimate the uncertainty in the model's output due to the uncertainty in the model's input parameters. This treatment is referred to as uncertainty Analysis, and is generally complemented with a Sensitivity Analysis, which is aimed at the identification of the most influential system parameters. Often different sensitivity analysis techniques are used in similar contexts, and it would be useful to identify (a) whether certain technique(s) perform better than others and (b) when two or more techniques can provide complementary information. In this article a number of sensitivity analysis techniques are compared in the case of non-linear model responses. The test models originate from the context of the risk analysis for the disposal of radioactive waste, where sensitivity analysis plays a crucial role. The statistics taken in to consideration include: • Pearson Correlation Coefficient • Partial Correlation Coefficient.