The Open UniversitySkip to content

Identifying regression outliers and mixtures graphically

Cook, R. Dennis and Critchley, Frank (2000). Identifying regression outliers and mixtures graphically. Journal of the American Statistical Association, 95(451) pp. 781–794.

Google Scholar: Look up in Google Scholar


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.

Item Type: Journal Item
Copyright Holders: 2000 American Statistical Association
ISSN: 1537-274X
Keywords: central subspace; lurking variable; regression graphics; sliced average variance estimation; sliced inverse regression
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 23410
Depositing User: Sarah Frain
Date Deposited: 04 Apr 2011 14:22
Last Modified: 02 May 2018 13:16
Share this page:

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU