Direction estimation in single-index regressions

Xiangrong Yin, R. Dennis Cook

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


We propose a general dimension-reduction method that combines the ideas of likelihood, correlation, inverse regression and information theory. We do not require that the dependence be confined to particular conditional moments, nor do we place restrictions on the predictors or on the regression that are necessary for methods like ordinary least squares and sliced-inverse regression. Although we focus on single-index regressions, the underlying idea is applicable more generally. Illustrative examples are presented.

Original languageEnglish (US)
Pages (from-to)371-384
Number of pages14
Issue number2
StatePublished - Jun 1 2005


  • Central mean subspace
  • Central subspace
  • Dimension-reduction subspace
  • Regression graphics
  • Single-index model

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