On false discovery control under dependence

By Wei Biao Wu. 2008

89 citations recorded by the source corpus.

A popular framework for false discovery control is the random effects model in which the null hypotheses are assumed to be independent. This paper generalizes the random effects model to a conditional dependence model which allows dependence between null hypotheses. The dependence can be useful to characterize the spatial structure of the null hypotheses. Asymptotic properties of false discovery proportions and numbers of rejected hypotheses are explored and a large-sample distributional theory is obtained.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5e147ca57ea8ba09c571abc2fb75a02724c1b611

How researchers see this paper

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In Statistics and Probability

1. Tommaso Cai
2. Wenguang Sun
3. H. Muller
4. Jiashun Jin
5. Jianqing Fan
6. Harrison H. Zhou
7. M. Thulin
8. Jing Lei

Citations in the corpus

• Global Testing and Large-Scale Multiple Testing for High-Dimensional Covariance Structures (2017, background): “The effects of dependency on multiple testing procedures have been considered, for example, in Benjamini and Yekutieli (2001); Storey et al. (2004); Qiu et al. (2005); Farcomeni (2007); Wu (2008); Efron (2007); Sun and Cai (2009); Sun et al. (2015).”
• Large‐scale multiple testing under dependence (2009, methodology): “BH and AP are considered for comparison because they are known to control FDR when the hypotheses are generated from an underlying HMM (Wu, 2008).”