Performance Analysis of Tyler's Covariance Estimator

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 estimation error bounds depending on the number of samples n and the dimension p. We show that under mild conditions the squared Frobenius norm of the error of the inverse estimator decays like p
²
/n with high probability.