论文标题
R-NL：基于非线性收缩的椭圆分布的协方差矩阵估计
R-NL: Covariance Matrix Estimation for Elliptical Distributions based on Nonlinear Shrinkage
论文作者
论文摘要
我们将泰勒对分散矩阵的强大估计量与非线性收缩结合在一起。这种方法为椭圆模型中的分散矩阵提供了一个简单而快速的估计器，该矩阵对重尾和高尺寸都具有鲁棒性。我们证明了算法的迭代部分的融合，并在各种模拟场景中证明了估计器的良好性能。最后，一个经验应用程序展示了其在实际数据上的最新性能。
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.
