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Title
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Robust PCA for skewed data and its outlier map
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Author
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Abstract
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| The outlier sensitivity of classical principal component analysis (PCA) has spurred the development of robust techniques. Existing robust PCA methods like ROBPCA work best if the non-outlying data have an approximately symmetric distribution. When the original variables are skewed, too many points tend to be flagged as outlying. A robust PCA method is developed which is also suitable for skewed data. To flag the outliers a new outlier map is defined. Its performance is illustrated on real data from economics, engineering, and finance, and confirmed by a simulation study. |
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Language
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English
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Source (journal)
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Computational statistics and data analysis / International Association for Statistical Computing. - Amsterdam, 1983, currens
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Publication
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Amsterdam
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North-Holland
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2009
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ISSN
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0167-9473
[print]
1872-7352
[online]
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DOI
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10.1016/J.CSDA.2008.05.027
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Volume/pages
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53
:6
(2009)
, p. 2264-2274
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ISI
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000264907200024
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Full text (Publisher's DOI)
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