摘要：The problem of the non-robustness of the classical estimates in the setting of the
quadratic and linear discriminant analysis has been addressed by many authors:
Todorov et al. [19, 20], Chork and Rousseeuw [1], Hawkins and McLachlan [4],
He and Fung [5], Croux and Dehon [2], Hubert and Van Driessen [6]. To obtain
high breakdown these methods are based on high breakdown point estimators of lo-
cation and covariance matrix like MVE, MCD and S. Most of the authors use also
one step re-weighting after the high breakdown point estimation in order to obtain
increased efficiency. We propose to use M-iteration as described by Woodruff and
Rocke [22] instead, since this is the preferred means of achieving efficiency with high
breakdown. Further we experiment with the pairwise class of algorithms proposed
by Maronna and Zamar [10] which were not used up to now in the context of dis-
criminant analysis. The available methods for robust linear discriminant analysis are
compared on two real data sets and on a large scale simulation study. These methods
are implemented as R functions in the package for robust multivariate analysis rrcov.
关键词：discriminant analysis; robustness; MCD; S-estimates; M-estimates; R.
