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May 2014 Two Modeling Strategies for Empirical Bayes Estimation
Bradley Efron
Statist. Sci. 29(2): 285-301 (May 2014). DOI: 10.1214/13-STS455

Abstract

Empirical Bayes methods use the data from parallel experiments, for instance, observations $X_{k}\sim\mathcal{N}(\Theta_{k},1)$ for $k=1,2,\ldots,N$, to estimate the conditional distributions $\Theta_{k}|X_{k}$. There are two main estimation strategies: modeling on the $\theta$ space, called “$g$-modeling” here, and modeling on the $x$ space, called “$f$-modeling.” The two approaches are described and compared. A series of computational formulas are developed to assess their frequentist accuracy. Several examples, both contrived and genuine, show the strengths and limitations of the two strategies.

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Bradley Efron. "Two Modeling Strategies for Empirical Bayes Estimation." Statist. Sci. 29 (2) 285 - 301, May 2014. https://doi.org/10.1214/13-STS455

Information

Published: May 2014
First available in Project Euclid: 18 August 2014

zbMATH: 1332.62031
MathSciNet: MR3264543
Digital Object Identifier: 10.1214/13-STS455

Rights: Copyright © 2014 Institute of Mathematical Statistics

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Vol.29 • No. 2 • May 2014
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