This book gives a solid foundation in robustness to both the theoretical
and the applied statistician. The treatment is theoretical, but the stress is
on concepts, rather than on mathematical completeness. The level of
presentation is deliberately uneven: in some chapters simple cases are
treated with mathematical rigor; in others the results obtained in the
simple cases are transferred by analogy to more complicated situations
(like multiparameter regression and covariance matrix estimation), where
proofs are not always available (or are available only under unrealistically
severe assumptions). Also selected numerical algorithms for computing
robust estimates are described and, where possible, convergence proofs are
given.