Minimalist G-modelling: A comment on Efron

Published on 3 April 2019

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.

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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.

Authors

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Jiaying Gu

Working Paper details

DOI
10.1920/wp.cem.2019.1319
Publisher
The IFS

Suggested citation

Gu, J and Koenker, R. (2019). Minimalist G-modelling: A comment on Efron. London: The IFS. Available at: https://ifs.org.uk/publications/minimalist-g-modelling-comment-efron (accessed: 19 April 2024).

Grant

Advancing microdata models and methods

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