Title: Robust PCA for skewed data and its outlier map
Authors: Hubert, Mia
Rousseeuw, Peter
Verdonck, Tim # ×
Issue Date: 2009
Publisher: North-Holland Pub. Co.
Series Title: Computational statistics & data analysis vol:53 issue:6 pages:2264-2274
Abstract: 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.
ISSN: 0167-9473
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Leuven Statistics Research Centre (LStat)
Statistics Section
× corresponding author
# (joint) last author

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