Limit theorems for large dimensional sample means, sample covariance matrices and Hotelling's T^2 statistics

In this paper, we prove the central limit theorem for Hotelling's $T^2$ statistics when the dimension of the random vectors is proportional to the sample size via investigating asymptotic independence and random quadratic forms involving sample means and sample covariance matrices.