Variable selection for Gaussian process models in computer experiments

Crystal Linkletter, Derak Bingham, Nicholas Hengartner, David Higdon, Kenny Q. Ye

Research output: Contribution to specialist publicationArticle

106 Scopus citations


In many situations, simulation of complex phenomena requires a large number of inputs and is computationally expensive. Identifying the inputs that most impact the system so that these factors can be further investigated can be a critical step in the scientific endeavor. In computer experiments, it is common to use a Gaussian spatial process to model the output of the simulator. In this article we introduce a new, simple method for identifying active factors in computer screening experiments. The approach is Bayesian and only requires the generation of a new inert variable in the analysis; however, in the spirit of frequentist hypothesis testing, the posterior distribution of the inert factor is used as a reference distribution against which the importance of the experimental factors can be assessed. The methodology is demonstrated on an application in material science, a computer experiment from the literature, and simulated examples.

Original languageEnglish (US)
Number of pages13
Specialist publicationTechnometrics
StatePublished - Nov 2006


  • Computer simulation
  • Latin hypercube
  • Random field
  • Screening
  • Spatial process

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics


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