title: Multiple Testing under Dependence via Semiparametric Graphical Models.
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 off1- the density function of the test statistic under the alternative hypothesis. However in practice,f1is often heterogeneous, and cannot be estimated with a simple parametric distribution. We propose a novel semiparametric approach for multiple testing under dependence, which estimatesf1adaptively. 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.
authors.affiliation: Department of Computer Sciences, University of Wisconsin-Madison.
authors.forename: Jie
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Statistics, University of Wisconsin-Madison.
authors.forename: Chunming
authors.initials: C
authors.lastname: Zhang
authors.affiliation: Department of Radiology, University of Wisconsin-Madison.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Burnside
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison.
authors.forename: David
authors.initials: D
authors.lastname: Page