Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 185, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.csda.2023.107757
Keywords
Bayesian variable selection; Emulator; Kriging; Median probability criterion
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Gaussian process models are commonly used in computer experiment analysis, but existing solutions for variable selection are unsatisfactory. We propose an indicator-based Bayesian variable selection procedure that considers the effects from both the mean and covariance functions. The performance of this method is evaluated through simulations and real applications in computer experiments.
Gaussian process (GP) models are commonly used in the analysis of computer experiments. Variable selection in GP models is of significant scientific interest but existing solutions remain unsatisfactory. For each variable in a GP model, there are two potential effects with different implications: one is on the mean function, and the other is on the covariance function. However, most of the existing research on variable selection for GP models has focused only on one of the effects. To tackle this problem, we propose an indicator-based Bayesian variable selection procedure to take into account the effects from both the mean and covariance functions. A variable is defined to be inactive if both effects are not significant, and an indicator is used to represent the variable being active or not. For active variables, the proposed method adopts different prior assumptions to capture the two effects. The performance of the proposed method is evaluated by both simulations and real applications in computer experiments.(c) 2023 Elsevier B.V. All rights reserved.
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