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Provable More Data Hurt in High Dimensional Least Squares Estimator
Zeng Li, Chuanlong Xie, Qinwen Wang · 2020-08-14 · via cs.LG updates on arXiv.org

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.

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