# Empirical Bayes mean estimation with nonparametric errors via order statistic regression on replicated data

arXiv: https://arxiv.org/abs/1911.05970 · HTML: https://mlsanity.com/paper/1911.05970/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [Data fission: splitting a single data point](https://mlsanity.com/paper/2112.11079/) (2021)
- [Fast Computation of Leave-One-Out Cross-Validation for k-NN Regression](https://mlsanity.com/paper/2405.04919/) (2024)
- [Empirical Bayes Estimation with Side Information: A Nonparametric Integrative Tweedie Approach](https://mlsanity.com/paper/2308.05883/) (2023)

## Abstract (arXiv)

We study empirical Bayes estimation of the effect sizes of N units from K noisy observations on each unit. We show that it is possible to achieve near-Bayes optimal mean squared error, without any assumptions or knowledge about the effect size distribution or the noise. The noise distribution can be heteroskedastic and vary arbitrarily from unit to unit. Our proposal, which we call Aurora, leverages the replication inherent in the K observations per unit and recasts the effect size estimation problem as a general regression problem. Aurora with linear regression provably matches the performance of a wide array of estimators including the sample mean, the trimmed mean, the sample median, as well as James-Stein shrunk versions thereof. Aurora automates effect size estimation for Internet-scale datasets, as we demonstrate on data from a large technology firm.