The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Control of the mean number of false discoveries, Bonferroni and stability of multiple testing
Open accessAbstract
The Bonferroni multiple testing procedure is commonly perceived as being overly conservative in large-scale simultaneous testing situations such as those that arise in microarray data analysis. The objective of the present study is to show that this popular belief is due to overly stringent requirements that are typically imposed on the procedure rather than to its conservative nature. To get over its notorious conservatism, we advocate using the Bonferroni selection rule as a procedure that controls the per family error rate (PFER). The present paper reports the first study of stability properties of the Bonferroni and Benjamini–Hochberg procedures. The Bonferroni procedure shows a superior stability in terms of the variance of both the number of true discoveries and the total number of discoveries, a property that is especially important in the presence of correlations between individual p-values. Its stability and the ability to provide strong control of the PFER make the Bonferroni procedure an attractive choice in microarray studies.
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Cites
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490
Identifying differentially expressed genes using false discovery rate controlling procedures
Reiner, A., Yekutieli, D., & Benjamini, Y. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btf877 · Cited by 1,765
Generalizations of the familywise error rate
Lehmann, E. L., & Romano, J. P. (2005) · The Annals of Statistics · doi:10.1214/009053605000000084 · Cited by 320 · cited 4×
“The main virtue of Bonf γ is its higher stability in terms of the variance of the total number of discoveries, a property that becomes increasingly important with the strength of correlation in the data [Qiu, Klebanov and Yakovlev (2005), Qiu et al. (2006)].”
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Cited by
Genome-wide association study of blood pressure and hypertension
Levy, D., Ehret, G. B., Rice, K., Verwoert, G. C., Launer, L. J., Dehghan, A., Glazer, N. L., Morrison, A. C., Johnson, A. D., Aspelund, T., et al. (2009) · Nature Genetics · doi:10.1038/ng.384 · Cited by 1,364
Psaty, B. M., O'Donnell, C. J., Gudnason, V., Lunetta, K. L., Folsom, A. R., Rotter, J. I., Uitterlinden, A. G., Harris, T. B., Witteman, J. C. M., & Boerwinkle, E. (2009) · Circulation: Cardiovascular Genetics · doi:10.1161/circgenetics.108.829747 · Cited by 599
Yang, Q., Wu, H., Guo, C., & Fox, C. S. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20497 · Cited by 142
Detecting intergene correlation changes in microarray analysis: a new approach to gene selection
Hu, R., Qiu, X., Glazko, G., Klebanov, L., & Yakovlev, A. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-20 · Cited by 79
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Metadata
- Authors
- Gordon, A., Glazko, G., Qiu, X., & Yakovlev, A.
- Venue
- The Annals of Applied Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2007-06-01
- Volume
- 1
- Issue
- 1
- Pages
- 179-190
- ISSN
- 1932-6157 (ISSN-L)
- OpenAlex
- W1970047903
- Cited by
- 176
- References
- 3
- FWCI
- 10.81
- Type
- journal article
- Language
- en
- Open access
- green
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