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 p²/n with high probability.