High-breakdown linear discriminant analysis

Hawkins, DM and McLachlan, GJ (1997) High-breakdown linear discriminant analysis. Journal of The American Statistical Association, 92 437: 136-143. doi:10.2307/2291457

Author Hawkins, DM
McLachlan, GJ
Title High-breakdown linear discriminant analysis
Journal name Journal of The American Statistical Association   Check publisher's open access policy
ISSN 0162-1459
Publication date 1997-01-01
Year available 1997
Sub-type Article (original research)
DOI 10.2307/2291457
Open Access Status
Volume 92
Issue 437
Start page 136
End page 143
Total pages 8
Place of publication ALEXANDRIA
Language eng
Abstract The classification rules of linear discriminant analysis are defined by the true mean vectors and the common covariance matrix of the populations from which the data come. Because these true parameters are generally unknown, they are commonly estimated by the sample mean vector and covariance matrix of the data in a training sample randomly drawn from each population. However, these sample statistics are notoriously susceptible to contamination by outliers, a problem compounded by the fact that the outliers may be invisible to conventional diagnostics. High-breakdown estimation is a procedure designed to remove this cause for concern by producing estimates that are immune to serious distortion by a minority of outliers, regardless of their severity. In this article we motivate and develop a high-breakdown criterion for linear discriminant analysis and give an algorithm for its implementation. The procedure is intended to supplement rather than replace the usual sample-moment methodology of discriminant analysis either by providing indications that the dataset is not seriously affected by outliers (supporting the usual analysis) or by identifying apparently aberrant points and giving resistant estimators that are not affected by them.
Keyword Statistics & Probability
Classification Rules
Minimum Covariance Determinant
Covariance Determinant Estimator
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
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Citation counts: TR Web of Science Citation Count  Cited 59 times in Thomson Reuters Web of Science Article | Citations
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Created: Tue, 14 Aug 2007, 02:41:31 EST