Robust PCA for skewed data and its outlier map

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.

Publication year:2009

Keywords:Mathematics, Applied mathematics, Computer science/information technology

BOF-keylabel:yes

BOF-publication weight:1

CSS-citation score:2

Authors from:Higher Education

Accessibility:Closed