  This chapter presents an introduction to robust statistics with applications
of a chemometric nature. Following a description of the basic ideas and
concepts behind robust statistics, including how robust estimators can be
conceived, the chapter builds up to the construction (and use) of robust
alternatives for some methods for multivariate analysis frequently used in
chemometrics, such as principal component analysis and partial least squares.
The chapter then provides an insight into how these robust methods can be used
or extended to classification. To conclude, the issue of validation of the
results is being addressed: it is shown how uncertainty statements associated
with robust estimates, can be obtained.
