  This paper investigates the finite-sample prediction risk of the
high-dimensional least squares estimator. We derive the central limit theorem
for the prediction risk when both the sample size and the number of features
tend to infinity. Furthermore, the finite-sample distribution and the
confidence interval of the prediction risk are provided. Our theoretical
results demonstrate the sample-wise nonmonotonicity of the prediction risk and
confirm "more data hurt" phenomenon.
