K-means inverse regression

C. Messen Setodji, R. Dennis Cook

Research output: Contribution to specialist publicationArticle

40 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 2004

Bibliographical note

Funding Information:
The authors thank the anonymous referees, the editor, and the associate editor for their useful comments and suggestions, which have greatly helped improve the quality of this article. This work was supported in part by the National Science Foundation grant DMS-01-03983.

Keywords

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

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