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Multiple predictor smoothing methods for sensitivity analysis: Description of techniques
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DOI：10.1016/j.ress.2006.10.012.png)
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The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. Then, in the second and concluding part of this presentation, the indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present. (C) 2006 Elsevier Ltd. All rights reserved.
Keyword:
additive models
epistemic uncertainty
locally weighted regression
nonparametric regression
projection pursuit regression
recursive partitioning regression
scatterplot smoothing
sensitivity analysis
stepwise selection
uncertainty analysis
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