Nonparametric Empirical Bayes Estimation on Heterogeneous Data

Abstract

The simultaneous estimation of many parameters based on data collected from corresponding studies is a key research problem that has received renewed attention in the high-dimensional setting. Many practical situations involve heterogeneous data where heterogeneity is captured by a nuisance parameter. Effectively pooling information across samples while correctly accounting for heterogeneity presents a significant challenge in large-scale estimation problems. We address this issue by introducing the ``Nonparametric Empirical Bayes Structural Tweedie" (NEST) estimator, which efficiently estimates the unknown effect sizes and properly adjusts for heterogeneity via a generalized version of Tweedie's formula. For the normal means problem, NEST simultaneously handles the two main selection biases introduced by heterogeneity: one, the selection bias in the mean, which cannot be effectively corrected without also correcting for, two, selection bias in the variance. We develop theory to show that NEST is asymptotically as good as the optimal Bayes rule that uniquely minimizes a weighted squared error loss. In our simulation studies NEST outperforms competing methods, with much efficiency gains in many settings. The proposed method is demonstrated on estimating the batting averages of baseball players and Sharpe ratios of mutual fund returns. Extensions to other members of the two-parameter exponential family are discussed.

Proof of Theorem 1 revised

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Cited by in corpus (6)

• On Post-Selection Inference in A/B Tests
• Nonparametric Empirical Bayes Estimation and Testing for Sparse and Heteroscedastic Signals
• A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family
• Multivariate, Heteroscedastic Empirical Bayes via Nonparametric Maximum Likelihood
• Heterocedasticity-Adjusted Ranking and Thresholding for Large-Scale Multiple Testing
• An Empirical Bayes Method for Chi-Squared Data