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:我们把泰勒对分散矩阵的强力估计与非线性缩水结合起来。 这种方法可以提供一个简单而快速的分布矩阵估计器, 用于对重尾和高维都很强的椭圆模型。 我们证明了我们算法的迭代部分的趋同,并展示了测量器在各种模拟情景中的优异性能。 最后, 实验应用展示了它在真实数据上的最新性能 。</s>