Name
Abstract | ||
|---|---|---|
It has been shown that graphical models can be used to leverage the dependence in large-scale multiple testing problems with significantly improved performance (Sun & Cai, 2009; Liu et al., 2012). These graphical models are fully parametric and require that we know the parameterization of f 1 - the density function of the test statistic under the alternative hypothesis. However in practice, f 1 is often heterogeneous, and cannot be estimated with a simple parametric distribution. We propose a novel semiparametric approach for multiple testing under dependence, which estimates f 1 adaptively. This semiparametric approach exactly generalizes the local FDR procedure (Efron et al., 2001) and connects with the BH procedure (Benjamini & Hochberg, 1995). A variety of simulations show that our semiparametric approach outperforms classical procedures which assume independence and the parametric approaches which capture dependence. |
Year | Venue | Keywords |
|---|---|---|
2014 | ICML | biomedical research,bioinformatics |
Field | DocType | Volume |
Alternative hypothesis,Parametrization,Test statistic,Computer science,Multiple comparisons problem,Parametric statistics,Artificial intelligence,Graphical model,Probability density function,Machine learning | Conference | 2014 |
Citations | PageRank | References |
1 | 0.37 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Liu | 1 | 1 | 0.71 |
| Chunming Zhang | 2 | 7 | 1.88 |
| Elizabeth S. Burnside | 3 | 199 | 27.84 |
| David Page | 4 | 533 | 61.12 |