Multiple predictor smoothing methods for sensitivity analysis: Description of techniques

Curtis B. Storlie, Jon C. Helton

Research output: Contribution to journalArticle

124 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)28-54
Number of pages27
JournalReliability Engineering and System Safety
Volume93
Issue number1
DOIs
StatePublished - Jan 1 2008

Keywords

  • Additive models
  • Epistemic uncertainty
  • Locally weighted regression
  • Nonparametric regression
  • Projection pursuit regression
  • Recursive partitioning regression
  • Scatterplot smoothing
  • Sensitivity analysis
  • Stepwise selection
  • Uncertainty analysis

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Industrial and Manufacturing Engineering

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