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• W2983215512 abstract "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 Google data." @default.
• W2983215512 created "2019-11-22" @default.
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• W2983215512 date "2019-11-14" @default.
• W2983215512 modified "2023-09-27" @default.
• W2983215512 title "Empirical Bayes mean estimation with nonparametric errors via order statistic regression" @default.
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• W2983215512 hasPublicationYear "2019" @default.
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