High-dimensional CLTs for individual Mahalanobis distances

In this paper we derive central limit theorems for two different types of Mahalanobis distances in situations where the dimension of the parent variable increases proportionally with the sample size. It is shown that although the two estimators are closely related and behave similarly in nite dimensions, they have different convergence rates and are also centred at two different points in high-dimensional settings. The limiting distributions are shown to be valid under some general moment conditions and hence available in a wide range of applications.