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On the empirical bayes approach to the problem of multiple testing

Bogdan, Malgorzata LU ; Ghosh, Jayanta K. ; Ochman, Aleksandra and Tokdar, Surya T. (2007) In Quality and Reliability Engineering International 23(6). p.727-739
Abstract

We discuss the Empirical Bayes approach to the problem of multiple testing and compare it with a very popular frequentist method of Benjamini and Hochberg aimed at controlling the false discovery rate. Our main focus is the 'sparse mixture' case, when only a small proportion of tested hypotheses is expected to be false. The specific parametric model we consider is motivated by the application to detecting genes responsible for quantitative traits, but it can be used in a variety of other applications. We define some Parametric Empirical Bayes procedures for multiple testing and compare them with the Benjamini and Hochberg method using computer simulations. We explain some similarities between these two approaches by placing them within... (More)

We discuss the Empirical Bayes approach to the problem of multiple testing and compare it with a very popular frequentist method of Benjamini and Hochberg aimed at controlling the false discovery rate. Our main focus is the 'sparse mixture' case, when only a small proportion of tested hypotheses is expected to be false. The specific parametric model we consider is motivated by the application to detecting genes responsible for quantitative traits, but it can be used in a variety of other applications. We define some Parametric Empirical Bayes procedures for multiple testing and compare them with the Benjamini and Hochberg method using computer simulations. We explain some similarities between these two approaches by placing them within the same framework of threshold tests with estimated critical values.

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Please use this url to cite or link to this publication:
author
; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Empirical Bayes, False discovery rate, Multiple testing
in
Quality and Reliability Engineering International
volume
23
issue
6
pages
13 pages
publisher
John Wiley & Sons Inc.
external identifiers
  • scopus:36749032653
ISSN
0748-8017
DOI
10.1002/qre.876
language
English
LU publication?
no
id
438fcc4e-3462-40e1-bbcd-c030ac28225a
date added to LUP
2023-12-08 09:22:50
date last changed
2023-12-11 10:37:14
@article{438fcc4e-3462-40e1-bbcd-c030ac28225a,
  abstract     = {{<p>We discuss the Empirical Bayes approach to the problem of multiple testing and compare it with a very popular frequentist method of Benjamini and Hochberg aimed at controlling the false discovery rate. Our main focus is the 'sparse mixture' case, when only a small proportion of tested hypotheses is expected to be false. The specific parametric model we consider is motivated by the application to detecting genes responsible for quantitative traits, but it can be used in a variety of other applications. We define some Parametric Empirical Bayes procedures for multiple testing and compare them with the Benjamini and Hochberg method using computer simulations. We explain some similarities between these two approaches by placing them within the same framework of threshold tests with estimated critical values.</p>}},
  author       = {{Bogdan, Malgorzata and Ghosh, Jayanta K. and Ochman, Aleksandra and Tokdar, Surya T.}},
  issn         = {{0748-8017}},
  keywords     = {{Empirical Bayes; False discovery rate; Multiple testing}},
  language     = {{eng}},
  number       = {{6}},
  pages        = {{727--739}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Quality and Reliability Engineering International}},
  title        = {{On the empirical bayes approach to the problem of multiple testing}},
  url          = {{http://dx.doi.org/10.1002/qre.876}},
  doi          = {{10.1002/qre.876}},
  volume       = {{23}},
  year         = {{2007}},
}