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Optimal detection of weak positive latent dependence between two sequences of multiple tests.


ABSTRACT: It is frequently of interest to jointly analyze two paired sequences of multiple tests. This paper studies the problem of detecting whether there are more pairs of tests that are significant in both sequences than would be expected by chance. The asymptotic detection boundary is derived in terms of parameters such as the sparsity of non-null cases in each sequence, the effect sizes of the signals, and the magnitude of the dependence between the two sequences. A new test for detecting weak dependence is also proposed, shown to be asymptotically adaptively optimal, studied in simulations, and applied to study genetic pleiotropy in 10 pediatric autoimmune diseases.

SUBMITTER: Zhao SD 

PROVIDER: S-EPMC5711487 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Optimal detection of weak positive latent dependence between two sequences of multiple tests.

Zhao Sihai Dave SD   Cai T Tony TT   Li Hongzhe H  

Journal of multivariate analysis 20170714


It is frequently of interest to jointly analyze two paired sequences of multiple tests. This paper studies the problem of detecting whether there are more pairs of tests that are significant in both sequences than would be expected by chance. The asymptotic detection boundary is derived in terms of parameters such as the sparsity of non-null cases in each sequence, the effect sizes of the signals, and the magnitude of the dependence between the two sequences. A new test for detecting weak depend  ...[more]

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