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February 2011 Multiple testing via FDRL for large-scale imaging data
Chunming Zhang, Jianqing Fan, Tao Yu
Ann. Statist. 39(1): 613-642 (February 2011). DOI: 10.1214/10-AOS848


The multiple testing procedure plays an important role in detecting the presence of spatial signals for large-scale imaging data. Typically, the spatial signals are sparse but clustered. This paper provides empirical evidence that for a range of commonly used control levels, the conventional FDR procedure can lack the ability to detect statistical significance, even if the p-values under the true null hypotheses are independent and uniformly distributed; more generally, ignoring the neighboring information of spatially structured data will tend to diminish the detection effectiveness of the FDR procedure. This paper first introduces a scalar quantity to characterize the extent to which the “lack of identification phenomenon” (LIP) of the FDR procedure occurs. Second, we propose a new multiple comparison procedure, called FDRL, to accommodate the spatial information of neighboring p-values, via a local aggregation of p-values. Theoretical properties of the FDRL procedure are investigated under weak dependence of p-values. It is shown that the FDRL procedure alleviates the LIP of the FDR procedure, thus substantially facilitating the selection of more stringent control levels. Simulation evaluations indicate that the FDRL procedure improves the detection sensitivity of the FDR procedure with little loss in detection specificity. The computational simplicity and detection effectiveness of the FDRL procedure are illustrated through a real brain fMRI dataset.


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Chunming Zhang. Jianqing Fan. Tao Yu. "Multiple testing via FDRL for large-scale imaging data." Ann. Statist. 39 (1) 613 - 642, February 2011.


Published: February 2011
First available in Project Euclid: 15 February 2011

MathSciNet: MR2797858
Digital Object Identifier: 10.1214/10-AOS848

Primary: 62G10 , 62H35
Secondary: 62E20 , 62P10

Keywords: Brain fMRI , False discovery rate , median filtering , p-value , Sensitivity , specificity

Rights: Copyright © 2011 Institute of Mathematical Statistics


Vol.39 • No. 1 • February 2011
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