  We investigate the performance of robust estimates of multivariate location
under nonstandard data contamination models such as componentwise outliers
(i.e., contamination in each variable is independent from the other variables).
This model brings up a possible new source of statistical error that we call
"propagation of outliers." This source of error is unusual in the sense that it
is generated by the data processing itself and takes place after the data has
been collected. We define and derive the influence function of robust
multivariate location estimates under flexible contamination models and use it
to investigate the effect of propagation of outliers. Furthermore, we show that
standard high-breakdown affine equivariant estimators propagate outliers and
therefore show poor breakdown behavior under componentwise contamination when
the dimension $d$ is high.
