  We propose a class of robust estimates for multivariate linear models. Based
on the approach of MM estimation (Yohai 1987), we estimate the regression
coefficients and the covariance matrix of the errors simultaneously. These
estimates have both high breakdown point and high asymptotic efficiency under
Gaussian errors. We prove consistency and asymptotic normality assuming errors
with an elliptical distribution. We describe an iterative algorithm for the
numerical calculation of these estimates. The advantages of the proposed
estimates over their competitors are demonstrated through both simulated and
real data.
