  Real data often contain anomalous cases, also known as outliers. These may
spoil the resulting analysis but they may also contain valuable information. In
either case, the ability to detect such anomalies is essential. A useful tool
for this purpose is robust statistics, which aims to detect the outliers by
first fitting the majority of the data and then flagging data points that
deviate from it. We present an overview of several robust methods and the
resulting graphical outlier detection tools. We discuss robust procedures for
univariate, low-dimensional, and high-dimensional data, such as estimating
location and scatter, linear regression, principal component analysis,
classification, clustering, and functional data analysis. Also the challenging
new topic of cellwise outliers is introduced.
