Statistical Science

Comment: Minimalist $g$-Modeling

Roger Koenker and Jiaying Gu

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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.

Article information

Source
Statist. Sci., Volume 34, Number 2 (2019), 209-213.

Dates
First available in Project Euclid: 19 July 2019

Permanent link to this document
https://projecteuclid.org/euclid.ss/1563501634

Digital Object Identifier
doi:10.1214/19-STS706

Mathematical Reviews number (MathSciNet)
MR3983321

Zentralblatt MATH identifier
07110689

Keywords
Nonparametric maximum likelihood mixture model convex optimization

Citation

Koenker, Roger; Gu, Jiaying. Comment: Minimalist $g$-Modeling. Statist. Sci. 34 (2019), no. 2, 209--213. doi:10.1214/19-STS706. https://projecteuclid.org/euclid.ss/1563501634


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