Empirical bayes and selective inference
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Published version
Author(s)
Rasines, Daniel García
Young, G Alastair
Type
Journal Article
Abstract
We review the empirical Bayes approach to large-scale inference. In the context of the problem of inference for a high-dimensional normal mean, empirical Bayes methods are advocated as they exhibit risk-reducing shrinkage, while establishing appropriate control of frequentist properties of the inference. We elucidate these frequentist properties and evaluate the protection that empirical Bayes provides against selection bias.
Date Issued
2022-10-01
Date Acceptance
2022-01-03
Citation
Journal of the Indian Institute of Science, 2022, 102, pp.1205-1217
ISSN
0970-4140
Publisher
Springer Science and Business Media LLC
Start Page
1205
End Page
1217
Journal / Book Title
Journal of the Indian Institute of Science
Volume
102
Copyright Statement
© 2022 The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://link.springer.com/article/10.1007/s41745-022-00286-0
Publication Status
Published
Date Publish Online
2022-03-07