FarmTest: Factor-adjusted robust multiple testing with approximate false discovery control

Jianqing Fan Yuan Ke Qiang Sun Wen-Xin Zhou

Statistics Theory and Methods mathscidoc:1912.43385

Journal of the American Statistical Association, 1-29, 2019.3
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
No keywords uploaded!
[ Download ] [ 2019-12-21 11:41:02 uploaded by Jianqing_Fan ] [ 250 downloads ] [ 0 comments ]
@inproceedings{jianqing2019farmtest:,
  title={FarmTest: Factor-adjusted robust multiple testing with approximate false discovery control},
  author={Jianqing Fan, Yuan Ke, Qiang Sun, and Wen-Xin Zhou},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114102548861945},
  booktitle={Journal of the American Statistical Association},
  pages={1-29},
  year={2019},
}
Jianqing Fan, Yuan Ke, Qiang Sun, and Wen-Xin Zhou. FarmTest: Factor-adjusted robust multiple testing with approximate false discovery control. 2019. In Journal of the American Statistical Association. pp.1-29. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114102548861945.
Please log in for comment!
 
 
Contact us: office-iccm@tsinghua.edu.cn | Copyright Reserved