Dimension estimation in a spiked covariance model using high-dimensional data augmentation
提出一种高维数据增强的维度估计方法，通过引入增广噪声变量来估计维度，理论证明在高维场景下具有一致性，模拟和实际数据验证了其优于现有方法。
Summary We propose a modified, high-dimensional version of a recent dimension estimation procedure that determines the dimension via the introduction of augmented noise variables into the data. Our asymptotic results show that the proposal is consistent in wide, high-dimensional scenarios, and further shed light on why the original method breaks down when the dimension of either the data or the augmentation becomes too large. Simulations and real data are used to demonstrate the superiority of the proposal to competitors both under and outside of the theoretical model.