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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 of f₁ - the density function of the test statistic under the alternative hypothesis. However in practice, f₁ 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 f₁ 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.

SUBMITTER: Liu J

PROVIDER: S-EPMC4190841 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Multiple Testing under Dependence via Semiparametric Graphical Models.

Liu Jie J Zhang Chunming C Burnside Elizabeth E Page David D

JMLR workshop and conference proceedings 20141201 2

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 f1 - the density function of the test statistic under the alternative hypothesis. However in practice, f1 is often heterogeneous, and cannot be estimated with a simple parame ...[more]

PMID: 25309970

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