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Empirical Bayes methods for controlling the false discovery rate with dependent data
- Source :
- Regina Liu, William Strawderman and Cun-Hui Zhang, eds., Complex Datasets and Inverse Problems: Tomography, Networks and Beyond (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2007), 151-160
- Publication Year :
- 2007
- Publisher :
- Institute of Mathematical Statistics, 2007.
-
Abstract
- False discovery rate (FDR) has been widely used as an error measure in large scale multiple testing problems, but most research in the area has been focused on procedures for controlling the FDR based on independent test statistics or the properties of such procedures for test statistics with certain types of stochastic dependence. Based on an approach proposed in Tang and Zhang (2005), we further develop in this paper empirical Bayes methods for controlling the FDR with dependent data. We implement our methodology in a time series model and report the results of a simulation study to demonstrate the advantages of the empirical Bayes approach.<br />Published at http://dx.doi.org/10.1214/074921707000000111 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)
- Subjects :
- False discovery rate
FOS: Computer and information sciences
62C12
62C10
Computer science
computer.software_genre
01 natural sciences
Methodology (stat.ME)
010104 statistics & probability
03 medical and health sciences
Bayes' theorem
Statistics
0101 mathematics
Time series
Statistics - Methodology
030304 developmental biology
Statistical hypothesis testing
62C25
multiple comparisons
0303 health sciences
Uniformly most powerful test
conditional false discovery rate
62H15, 62C10, 62C12, 62C25 (Primary)
Bayes rule
Multiple comparisons problem
62H15
Data mining
false discovery rate
most powerful test
time series
computer
empirical Bayes
dependent data
Subjects
Details
- Language :
- English
- ISSN :
- 07492170
- Database :
- OpenAIRE
- Journal :
- Regina Liu, William Strawderman and Cun-Hui Zhang, eds., Complex Datasets and Inverse Problems: Tomography, Networks and Beyond (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2007), 151-160
- Accession number :
- edsair.doi.dedup.....93fe19f952926d6ffb7077cdcbe14e49