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Abstract Details
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Activity Number:
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463
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Physical and Engineering Sciences
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| Abstract - #306710 |
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Title:
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Estimating Sensitivity Indices for Computer Experiment Output
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Author(s):
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Thomas Santner*+ and Angela Dean and Josh Svenson and Hyejung Moon
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Companies:
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The Ohio State University and The Ohio State University/Southampton University and JPMorgan Chase & Co. and The Bank of Korea
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Address:
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404 Cockins, Columbus, OH, 43210-1247, United States
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Keywords:
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Computer Experiments ;
Process-based estimation ;
Total sensitivity indices ;
Main-effect sensitivity indices
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Abstract:
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This talk shows how to compute for empirical Bayes and Fully Bayes estimators of Sobol main effect and total effect global sensitivity indices for a broad class of regression plus stationary Gaussian process models, using the Gaussian, cubic, or Bohman correlation functions. It is shown how to restrict the parameter space for the compactly supported cubic and Bohman correlation functions so that at least a given proportion of the training data correlation entries are zero. This feature is important in the situation where the set of training data is large. Formulae are given in a form suitable for computation, and the estimation methods are illustrated and compared via examples. (joint work with Joshua Svenson, Angela Dean, and Hyejung Moon)
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