  This paper analyzes the performance of Tyler's M-estimator of the scatter
matrix in elliptical populations. We focus on the non-asymptotic setting and
derive the estimation error bounds depending on the number of samples n and the
dimension p. We show that under quite mild conditions the squared Frobenius
norm of the error of the inverse estimator decays like p^2/n with high
probability.
