  We consider median of means (MOM) versions of the Stahel-Donoho outlyingness
(SDO) [stahel 1981, donoho 1982] and of Median Absolute Deviation (MAD)
functions to construct subgaussian estimators of a mean vector under
adversarial contamination and heavy-tailed data. We develop a single analysis
of the MOM version of the SDO which covers all cases ranging from the Gaussian
case to the L2 case. It is based on isomorphic and almost isometric properties
of the MOM versions of SDO and MAD. This analysis also covers cases where the
mean does not even exist but a location parameter does; in those cases we still
recover the same subgaussian rates and the same price for adversarial
contamination even though there is not even a first moment. These properties
are achieved by the classical SDO median and are therefore the first
non-asymptotic statistical bounds on the Stahel-Donoho median complementing the
$\sqrt{n}$-consistency [maronna 1995] and asymptotic normality [Zuo, Cui, He,
2004] of the Stahel-Donoho estimators. We also show that the MOM version of MAD
can be used to construct an estimator of the covariance matrix under only a
L2-moment assumption or of a scale parameter if a second moment does not exist.
