  We formalize notions of robustness for composite estimators via the notion of
a breakdown point. A composite estimator successively applies two (or more)
estimators: on data decomposed into disjoint parts, it applies the first
estimator on each part, then the second estimator on the outputs of the first
estimator. And so on, if the composition is of more than two estimators.
Informally, the breakdown point is the minimum fraction of data points which if
significantly modified will also significantly modify the output of the
estimator, so it is typically desirable to have a large breakdown point. Our
main result shows that, under mild conditions on the individual estimators, the
breakdown point of the composite estimator is the product of the breakdown
points of the individual estimators. We also demonstrate several scenarios,
ranging from regression to statistical testing, where this analysis is easy to
apply, useful in understanding worst case robustness, and sheds powerful
insights onto the associated data analysis.

Machine Learning;Machine Learning