The eigenvector empirical spectral distribution (VESD) is a useful tool in studying the limiting behavior of eigenvalues and eigenvectors of covariance matrices. In this paper, we study the convergence rate of the VESD of sample covariance matrices to the Marchenko-Pastur (MP) distribution. Consider sample covariance matrices of the form $XX^*$, where $X=(x_{ij})$ is an $M\times N$ random matrix whose entries are independent (but not necessarily identically distributed) random variables with mean zero and variance $N^{-1}$. We show that the Kolmogorov distance between the expected VESD and the MP distribution is bounded by $N^{-1+\epsilon}$ for any fixed $\epsilon>0$, provided that the entries $\sqrt{N}x_{ij}$ have uniformly bounded 6th moment and that the dimension ratio $N/M$ converges to some constant $d\ne 1$. This result improves the previous one obtained in [33], which gives the convergence rate $O(N^{-1/2})$ assuming $i.i.d.$ $X$ entries, bounded 10th moment and $d>1$. Mor 查看全文>>