Robust empirical mean Estimators
  We study robust estimators of the mean of a probability measure $P$, called robust empirical mean estimators. This elementary construction is then used to revisit a problem of aggregation and a problem of estimator selection, extending these methods to not necessarily bounded collections of previous estimators. We consider then the problem of robust $M$-estimation. We propose a slightly more complicated construction to handle this problem and, as examples of applications, we apply our general approach to least-squares density estimation, to density estimation with K\"ullback loss and to a non-Gaussian, unbounded, random design and heteroscedastic regression problem. Finally, we show that our strategy can be used when the data are only assumed to be mixing. 
M. Lerasle and R. I. Oliveira
math.ST stat.TH