Transformed sufficient dimension reduction

T. Wang, X. Guo, L. Zhu, P. Xu

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


We propose a general framework for dimension reduction in regression to fill the gap between linear and fully nonlinear dimension reduction. Themain idea is to first transformeach of the raw predictors monotonically and then search for a low-dimensional projection in the space defined by the transformed variables. Both user-specified and data-driven transformations are suggested. In each case, the methodology is first discussed in generality and then a representative method is proposed and evaluated by simulation. The proposed methods are applied to a real dataset.

Original languageEnglish (US)
Pages (from-to)815-829
Number of pages15
Issue number4
StatePublished - Dec 1 2014

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • General Mathematics
  • Agricultural and Biological Sciences (miscellaneous)
  • General Agricultural and Biological Sciences
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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