On the empirical bayes approach to the problem of multiple testing
(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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- author
- Bogdan, Malgorzata LU ; Ghosh, Jayanta K. ; Ochman, Aleksandra and Tokdar, Surya T.
- publishing date
- 2007-10
- 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}}, }