id: 05874507
dt: j
an: 05874507
au: Zhang, Chunming; Fan, Jianqing; Yu, Tao
ti: Multiple testing via $\mathrm{FDR}_L$ for large-scale imaging data.
so: Ann. Stat. 39, No. 1, 613-642 (2011).
py: 2011
pu: Institute of Mathematical Statistics, Beachwood, OH
la: EN
cc: 
ut: brain fMRI; false discovery rate; median filtering; $p$-value; sensitivity;
    specificity
ci: 
li: doi:10.1214/10-AOS848
ab: Summary: 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 false discovery rate (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 FDR$_L$, to
    accommodate the spatial information of neighboring $p$-values, via a
    local aggregation of $p$-values. Theoretical properties of the FDR$_L$
    procedure are investigated under weak dependence of $p$-values. It is
    shown that the FDR$_L$ procedure alleviates the LIP of the FDR
    procedure, thus substantially facilitating the selection of more
    stringent control levels. Simulation evaluations indicate that the
    FDR$_L$ procedure improves the detection sensitivity of the FDR
    procedure with little loss in detection specificity. The computational
    simplicity and detection effectiveness of the FDR$_L$ procedure are
    illustrated through a real brain fMRI dataset.
rv: