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：我们将泰勒色散矩阵的稳健估计器与非线性收缩相结合。该方法为椭圆模型中的色散矩阵提供了一种简单且快速的估计器，能够同时应对重尾分布和高维情况。我们证明了算法迭代部分的收敛性，并展示了该估计器在多种模拟场景中的优越性能。最后，通过实证应用验证了其在真实数据上的先进表现。