  Whilst influence functions for linear discriminant analysis (LDA) have been
found for a single discriminant when dealing with two groups, until now these
have not been derived in the setting of a general number of groups. In this
paper we explore the relationship between Sliced Inverse Regression (SIR) and
LDA, and exploit this relationship to develop influence functions for LDA from
those already derived for SIR. These influence functions can be used to
understand robustness properties of LDA and also to detect influential
observations in practice. We illustrate the usefulness of these via their
application to a real data set.
