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Detection of multiple influential cases in principal component analysis: A graphical technique

Lookup NU author(s): Emeritus Professor Julian Morris, Professor Elaine Martin

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Abstract

The detection of multiple influential cases in principal component analysis through case-deletion necessitates the investigation of a large number of combinations of observation cases to locate the influential cases. In this article, a graphical technique is proposed for the detection of multiple influential cases in principal component analysis based on the empirical influence curve. Detection of multiple influential cases is undertaken by visually inspecting outlying points in a series of two-dimensional diagnostic plots. It is shown that the proposed graphical method is easily interpretable and incurs very low computational costs. A practical example based on soil composition data that has been used in previous studies into multiple influential cases is used to illustrate the proposed graphical technique.


Publication metadata

Author(s): Li B, Morris AJ, Martin EB

Publication type: Article

Publication status: Published

Journal: Communications in Statistics Part B: Simulation and Computation

Year: 2003

Volume: 32

Issue: 2

Pages: 489-503

Print publication date: 01/05/2003

ISSN (print): 0361-0918

ISSN (electronic): 1532-4141

Publisher: Taylor & Francis Inc.

URL: http://dx.doi.org/10.1081/SAC-120017503

DOI: 10.1081/SAC-120017503


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