K-means inverse regression

C. Messen Setodji, R. Dennis Cook

Research output: Contribution to specialist publicationArticle

37 Scopus citations

Abstract

Li suggested the method of sliced inverse regression for dimension reduction in regressions with a univariate response. In this article we extend that method to multivariate regressions by introducing a new way of performing the slicing. This method applies for any number of response variables and may be particularly useful at the outset of an analysis, before positing a multivariate model. The emphasis is on application; no new asymptotic theory is presented.

Original languageEnglish (US)
Pages421-429
Number of pages9
Volume46
No4
Specialist publicationTechnometrics
DOIs
StatePublished - Nov 1 2004

Keywords

  • Central subspaces
  • Dimension reduction
  • Functional data analysis
  • K-means clustering
  • Multivariate regression
  • Regression graphics
  • Sliced inverse regression

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