Trace Pursuit: A General Framework for Model-Free Variable Selection

Zhou Yu, Yuexiao Dong, Lixing ZHU*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

We propose trace pursuit for model-free variable selection under the sufficient dimension-reduction paradigm. Two distinct algorithms are proposed: stepwise trace pursuit and forward trace pursuit. Stepwise trace pursuit achieves selection consistency with fixed p. Forward trace pursuit can serve as an initial screening step to speed up the computation in the case of ultrahigh dimensionality. The screening consistency property of forward trace pursuit based on sliced inverse regression is established. Finite sample performances of trace pursuit and other model-free variable selection methods are compared through numerical studies. Supplementary materials for this article are available online.

Original languageEnglish
Pages (from-to)813-821
Number of pages9
JournalJournal of the American Statistical Association
Volume111
Issue number514
DOIs
Publication statusPublished - 2 Apr 2016

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Directional regression
  • Selection consistency
  • Sliced average variance estimation
  • Sliced inverse regression
  • Stepwise regression

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