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Activity Number:
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348
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Type:
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Invited
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Date/Time:
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Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Technometrics
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| Abstract - #302763 |
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Title:
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Variable Selection in Bayesian Smoothing Spline ANOVA Models: Application to Deterministic Computer Codes
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Author(s):
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Brian J. Reich*+ and Curtis Storlie and Howard D. Bondell
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Companies:
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North Carolina State University and University of New Mexico and North Carolina State University
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Address:
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Campus Box 8203, Raleigh, NC, 27695,
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Keywords:
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Bayesian hierarchical modeling ; Nonparametric regression ; Markov Chain Monte Carlo ; Smoothing splines ANOVA ; Variable selection
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Abstract:
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With many predictors, choosing an appropriate subset of the covariates is a crucial, and difficult, step in nonparametric regression. We propose a Bayesian nonparametric regression model for curve-fitting and variable selection. We use the smoothing spline ANOVA framework to decompose the regression function into interpretable main effect and interaction functions. Stochastic search variable selection via MCMC sampling is used to search for models that fit the data well. Also, we show that variable selection is highly-sensitive to hyperparameter choice and develop a technique to select hyperparameters that control the long-run false positive rate. The method is used to build an emulator for a complex computer model for two-phase fluid flow.
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