TY - GEN
T1 - Multiple predictor smoothing methods for sensitivity analysis
AU - Storlie, Curtis B.
AU - Helton, Jon C.
PY - 2005
Y1 - 2005
N2 - 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 (GAMs), (iii) projection pursuit regression (PP_REG), and (iv) recursive partitioning regression (RP_REG). 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 response surface regression when nonlinear relationships between model inputs and model predictions are present.
AB - 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 (GAMs), (iii) projection pursuit regression (PP_REG), and (iv) recursive partitioning regression (RP_REG). 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 response surface regression when nonlinear relationships between model inputs and model predictions are present.
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U2 - 10.1109/WSC.2005.1574256
DO - 10.1109/WSC.2005.1574256
M3 - Conference contribution
AN - SCOPUS:33846684615
SN - 0780395204
SN - 9780780395206
T3 - Proceedings - Winter Simulation Conference
SP - 231
EP - 239
BT - Proceedings of the 2005 Winter Simulation Conference
T2 - 2005 Winter Simulation Conference
Y2 - 4 December 2005 through 7 December 2005
ER -