Large-Scale Hypothesis Testing is a statistical framework for simultaneously evaluating a large number of hypotheses, typically in high-dimensional data settings, where the scale necessitates specialized methods to control error rates and borrow information across tests.
185
Publications
32.2K
Citations
344
Authors
205
Institutions
Publications per year
2017–2026
44
Parents
Publications
185
False discovery rates: a new deal
Matthew Stephens · Biostatistics · 2016 · 1.1K citations · Full text
Large-Scale Simultaneous Hypothesis Testing
Bradley Efron · Journal of the American Statistical Association · 2004 · 1.1K citations
1–5 of 185
Authors
344
- TC
8
h-index
- EW
6
h-index
- JF
5
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- WS
4
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- BE
3
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1–5 of 344
Institutions
205
Stanford University
Stanford, United States
9 h-index
15 publications
2.2K citations
Princeton University
Princeton, United States
8 h-index
13 publications
2.3K citations
University of Pennsylvania
Philadelphia, United States
8 h-index
13 publications
1.3K citations
Harvard University
Cambridge, United States
6 h-index
9 publications
857 citations
University of Chicago
Chicago, United States
5 h-index
7 publications
1.9K citations
Fudan University
Shanghai, China
4 h-index
7 publications
1.2K citations
Educational Testing Service
Princeton, United States
4 h-index
4 publications
172 citations
University of California San Diego
San Diego, United States
4 h-index
4 publications
10.6K citations
1–8 of 205
Venues
- Journal of the American Statistical Association
17 publications · 4K citations
17
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- The Annals of Statistics
11 publications · 1.3K citations
10
h-index
- arXiv (Cornell University)
Repository · 8 publications · 164 citations
8
h-index
- Journal of the Royal Statistical Society Series B (Statistical Methodology)
7 publications · 794 citations
7
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- PLoS ONE
3 publications · 254 citations
3
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1–5