Computational statistics & data analysis vol:53 issue:6 pages: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.