Multiple predictor smoothing methods for sensitivity analysis.

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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: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. 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 ... continued below

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152 p.

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Helton, Jon Craig & Storlie, Curtis B. August 1, 2006.

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Description

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: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. 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.

Physical Description

152 p.

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  • Report No.: SAND2006-4693
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/893126 | External Link
  • Office of Scientific & Technical Information Report Number: 893126
  • Archival Resource Key: ark:/67531/metadc882337

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  • August 1, 2006

Added to The UNT Digital Library

  • Sept. 21, 2016, 2:29 a.m.

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  • Nov. 29, 2016, 9:24 p.m.

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Helton, Jon Craig & Storlie, Curtis B. Multiple predictor smoothing methods for sensitivity analysis., report, August 1, 2006; United States. (digital.library.unt.edu/ark:/67531/metadc882337/: accessed September 25, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.