The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Size, power and false discovery rates
Open accessAbstract
Modern scientific technology has provided a new class of large-scale simultaneous inference problems, with thousands of hypothesis tests to consider at the same time. Microarrays epitomize this type of technology, but similar situations arise in proteomics, spectroscopy, imaging, and social science surveys. This paper uses false discovery rate methods to carry out both size and power calculations on large-scale problems. A simple empirical Bayes approach allows the false discovery rate (fdr) analysis to proceed with a minimum of frequentist or Bayesian modeling assumptions. Closed-form accuracy formulas are derived for estimated false discovery rates, and used to compare different methodologies: local or tail-area fdr’s, theoretical, permutation, or empirical null hypothesis estimates. Two microarray data sets as well as simulations are used to evaluate the methodology, the power diagnostics showing why nonnull cases might easily fail to appear on a list of “significant” discoveries.
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
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A Direct Approach to False Discovery Rates
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Gene expression correlates of clinical prostate cancer behavior
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Empirical Bayes analysis of a microarray experiment
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Self-citationResampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment.
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Self-citationLee, M.-L. T., Kuo, F. C., Whitmore, G. A., & Sklar, J. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.18.9834 · Cited by 861
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Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk
Zhou, J., Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., & Troyanskaya, O. G. (2018) · Nature Genetics · doi:10.1038/s41588-018-0160-6 · Cited by 559
Andreassen, O. A., Djurovic, S., Thompson, W. K., Schork, A. J., Kendler, K. S., O’Donovan, M. C., Rujescu, D., Werge, T., van de Bunt, M., Morris, A. P., et al. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.01.001 · Cited by 506
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Simsek, D., Tiu, G. C., Flynn, R. A., Byeon, G. W., Leppek, K., Xu, A. F., Chang, H. Y., & Barna, M. (2017) · Cell · doi:10.1016/j.cell.2017.05.022 · Cited by 439
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Metadata
- Authors
- Efron, B.
- Venue
- The Annals of Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2007-08-01
- Volume
- 35
- Issue
- 4
- Pages
- 1351-1377
- ISSN
- 0090-5364 (ISSN-L)
- OpenAlex
- W2009660843
- Cited by
- 411
- References
- 33
- FWCI
- 16.22
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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