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FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control
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DOI：10.1080/01621459.2018.1527700.png)
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摘要
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Large-scale multiple testing with correlated and heavy-tailed data arises in a wide range of research areas from genomics, medical imaging to finance. Conventional methods for estimating the false discovery proportion (FDP) often ignore the effect of heavy-tailedness and the dependence structure among test statistics, and thus may lead to inefficient or even inconsistent estimation. Also, the commonly imposed joint normality assumption is arguably too stringent for many applications. To address these challenges, in this article we propose a factor-adjusted robust multiple testing (FarmTest) procedure for large-scale simultaneous inference with control of the FDP. We demonstrate that robust factor adjustments are extremely important in both controlling the FDP and improving the power. We identify general conditions under which the proposed method produces consistent estimate of the FDP. As a byproduct that is of independent interest, we establish an exponential-type deviation inequality for a robust U-type covariance estimator under the spectral norm. Extensive numerical experiments demonstrate the advantage of the proposed method over several state-of-the-art methods especially when the data are generated from heavy-tailed distributions. The proposed procedures are implemented in the R-package FarmTest. for this article are available online.
Keyword:
Factor adjustment
False discovery proportion
Huber loss
Large-scale multiple testing
Robustness
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Estimation of high dimensional mean regression in the absence of symmetry and light tail assumptions
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OAAI分享收藏PHASE TRANSITION AND REGULARIZED BOOTSTRAP IN LARGE-SCALE t-TESTS WITH FALSE DISCOVERY RATE CONTROL.png)
ANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏SUB-GAUSSIAN ESTIMATORS OF THE MEAN OF A RANDOM MATRIX WITH HEAVY-TAILED ENTRIESANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
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