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• W3100944228 abstract "In the context of large-scale multiple testing, hypotheses are often accompanied with certain prior information. In this paper, we present a single-index modulated (SIM) multiple testing procedure, which maintains control of the false discovery rate while incorporating prior information, by assuming the availability of a bivariate $p$-value, $(p_1,p_2)$, for each hypothesis, where $p_1$ is a preliminary $p$-value from prior information and $p_2$ is the primary $p$-value for the ultimate analysis. To find the optimal rejection region for the bivariate $p$-value, we propose a criteria based on the ratio of probability density functions of $(p_1,p_2)$ under the true null and nonnull. This criteria in the bivariate normal setting further motivates us to project the bivariate $p$-value to a single-index, $p(theta)$, for a wide range of directions $theta$. The true null distribution of $p(theta)$ is estimated via parametric and nonparametric approaches, leading to two procedures for estimating and controlling the false discovery rate. To derive the optimal projection direction $theta$, we propose a new approach based on power comparison, which is further shown to be consistent under some mild conditions. Simulation evaluations indicate that the SIM multiple testing procedure improves the detection power significantly while controlling the false discovery rate. Analysis of a real dataset will be illustrated." @default.
• W3100944228 created "2020-11-23" @default.
• W3100944228 creator A5011347842 @default.
• W3100944228 creator A5088692421 @default.
• W3100944228 date "2014-08-01" @default.
• W3100944228 modified "2023-10-17" @default.
• W3100944228 title "Single-index modulated multiple testing" @default.
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• W3100944228 doi "https://doi.org/10.1214/14-aos1222" @default.
• W3100944228 hasPublicationYear "2014" @default.
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