Robust PCA for skewed data and its outlier map
Peter Rousseeuw () and
Computational Statistics & Data Analysis, 2009, vol. 53, issue 6, 2264-2274
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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