# Provable More Data Hurt in High Dimensional Least Squares Estimator

arXiv: https://arxiv.org/abs/2008.06296 · HTML: https://mlsanity.com/paper/2008.06296/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality](https://mlsanity.com/paper/2208.03848/) (2022)

## Abstract (arXiv)

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.