Open Access
May 2019 Comment: Minimalist $g$-Modeling
Roger Koenker, Jiaying Gu
Statist. Sci. 34(2): 209-213 (May 2019). DOI: 10.1214/19-STS706

Abstract

Efron’s elegant approach to $g$-modeling for empirical Bayes problems is contrasted with an implementation of the Kiefer–Wolfowitz nonparametric maximum likelihood estimator for mixture models for several examples. The latter approach has the advantage that it is free of tuning parameters and consequently provides a relatively simple complementary method.

Citation

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Roger Koenker. Jiaying Gu. "Comment: Minimalist $g$-Modeling." Statist. Sci. 34 (2) 209 - 213, May 2019. https://doi.org/10.1214/19-STS706

Information

Published: May 2019
First available in Project Euclid: 19 July 2019

zbMATH: 07110689
MathSciNet: MR3983321
Digital Object Identifier: 10.1214/19-STS706

Keywords: Convex optimization , mixture model , Nonparametric maximum likelihood

Rights: Copyright © 2019 Institute of Mathematical Statistics

Vol.34 • No. 2 • May 2019
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