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Variable Selection in Bayesian Smoothing Spline ANOVA Models

Summary: [This abstract is based on the authors' abstract.] A Bayesian nonparametric regression model for curve fitting and variable selection is proposed for analyzing complex computer model output. The smoothing splines ANOVA framework is used to separate the regression function into main effects and interaction functions that can be interpreted. Stochastic search variable selection through Markov chain and Monte Carlo sampling locate models that fit the data well. The method is demonstrated on an emulator for a complex computer for two-phase fluid flow.

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  • Topics: Design of Experiments
  • Keywords: Bayesian methods, Hierarchical experiments, Markov chains, Monte Carlo methods, Regression, Nonparametric methods, Data smoothing, Spline functions, Analysis of variance (ANOVA), Variable selection
  • Author: Reich, Brian J.; Storlie, Curtis B.; Bondell; Howard D.
  • Journal: Technometrics