We combine Tyler's robust estimator of the dispersion matrix with nonlinear shrinkage. This approach delivers a simple and fast estimator of the dispersion matrix in elliptical models that is robust against both heavy tails and high dimensions. We prove convergence of the iterative part of our algorithm and demonstrate the favorable performance of the estimator in a wide range of simulation scenarios. Finally, an empirical application demonstrates its state-of-the-art performance on real data.

翻译：我们将泰勒离散矩阵鲁棒估计器与非线性收缩相结合。该方法为椭圆模型提供了一种简单且快速的离散矩阵估计器，能够同时应对重尾分布和高维场景。我们证明了算法迭代部分的收敛性，并通过广泛仿真场景验证了该估计器的优越性能。最后，实证应用展示了其在真实数据上的领先表现。