Empirical Bayes for the Reluctant Frequentist

Fuente: arXiv
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Hauptverfasser: Koenker, Roger, Gu, Jiaying
Format: Preprint
Veröffentlicht: 2024
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author Koenker, Roger
Gu, Jiaying
author_facet Koenker, Roger
Gu, Jiaying
contents Empirical Bayes methods offer valuable tools for a large class of compound decision problems. In this tutorial we describe some basic principles of the empirical Bayes paradigm stressing their frequentist interpretation. Emphasis is placed on recent developments of nonparametric maximum likelihood methods for estimating mixture models. A more extensive introductory treatment will eventually be available in \citet{kg24}. The methods are illustrated with an extended application to models of heterogeneous income dynamics based on PSID data.
format Preprint
id arxiv_https___arxiv_org_abs_2404_03422
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Empirical Bayes for the Reluctant Frequentist
Koenker, Roger
Gu, Jiaying
Methodology
Empirical Bayes methods offer valuable tools for a large class of compound decision problems. In this tutorial we describe some basic principles of the empirical Bayes paradigm stressing their frequentist interpretation. Emphasis is placed on recent developments of nonparametric maximum likelihood methods for estimating mixture models. A more extensive introductory treatment will eventually be available in \citet{kg24}. The methods are illustrated with an extended application to models of heterogeneous income dynamics based on PSID data.
title Empirical Bayes for the Reluctant Frequentist
topic Methodology
url https://arxiv.org/abs/2404.03422