Identifying regression outliers and mixtures graphically

R. Dennis Cook, Frank Critchley

Research output: Contribution to journalArticle

56 Scopus citations

Abstract

Regressions in practice can include outliers and other unknown subpopulation structures. For example, mixtures of regressions occur if there is an omitted categorical predictor, like gender or location, and different regressions occur within each category. The theory of regression graphics based on central subspaces can be used to construct graphical solutions to long-standing problems of this type. It is argued that in practice the central subspace automatically expands to incorporate outliers and regression mixtures. Thus methods of estimating the central subspace can be used to identify these phenomena, without specifying a model. Examples illustrating the power of the theory are presented.

Original languageEnglish (US)
Pages (from-to)781-794
Number of pages14
JournalJournal of the American Statistical Association
Volume95
Issue number451
DOIs
StatePublished - Sep 1 2000

Keywords

  • Central subspace
  • Lurking variable
  • Regression graphics
  • Sliced average variance estimation
  • Sliced inverse regression

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