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Consistent variable selection via the optimal discovery procedure in multiple testing

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  • Li Wang
  • Xingzhong Xu

Abstract

In this paper, we translate variable selection for linear regression into multiple testing, and select significant variables according to testing result. New variable selection procedures are proposed based on the optimal discovery procedure (ODP) in multiple testing. Due to ODP’s optimality, if we guarantee the number of significant variables included, it will include less non significant variables than marginal p-value based methods. Consistency of our procedures is obtained in theory and simulation. Simulation results suggest that procedures based on multiple testing have improvement over procedures based on selection criteria, and our new procedures have better performance than marginal p-value based procedures.

Suggested Citation

  • Li Wang & Xingzhong Xu, 2017. "Consistent variable selection via the optimal discovery procedure in multiple testing," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(13), pages 6303-6322, July.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:13:p:6303-6322
    DOI: 10.1080/03610926.2015.1069351
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