Transformed sufficient dimension reduction

T. Wang, X. Guo, Lixing ZHU, P. Xu

Research output: Contribution to journalJournal articlepeer-review

8 Citations (Scopus)


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
Pages (from-to)815-829
Number of pages15
Issue number4
Publication statusPublished - 1 Dec 2014

Scopus Subject Areas

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

User-Defined Keywords

  • Minimum average variance estimation
  • Monotone smoothing spline
  • Predictor transformation
  • Probability integral transformation
  • Sliced inverse regression


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