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Variable selection via a multi-stage strategy

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  • Jing Chang
  • Herbert K.H. Lee

Abstract

Variable selection for nonlinear regression is a complex problem, made even more difficult when there are a large number of potential covariates and a limited number of datapoints. We propose herein a multi-stage method that combines state-of-the-art techniques at each stage to best discover the relevant variables. At the first stage, an extension of the Bayesian Additive Regression tree is adopted to reduce the total number of variables to around 30. At the second stage, sensitivity analysis in the treed Gaussian process is adopted to further reduce the total number of variables. Two stopping rules are designed and sequential design is adopted to make best use of previous information. We demonstrate our approach on two simulated examples and one real data set.

Suggested Citation

  • Jing Chang & Herbert K.H. Lee, 2015. "Variable selection via a multi-stage strategy," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(4), pages 762-774, April.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:762-774
    DOI: 10.1080/02664763.2014.985640
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    References listed on IDEAS

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    1. Gramacy, Robert B & Lee, Herbert K. H, 2008. "Bayesian Treed Gaussian Process Models With an Application to Computer Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1119-1130.
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