Estimation of the false discovery proportion with unknown dependence

Jianqing Fan Xu Han

Statistics Theory and Methods mathscidoc:1912.43338

Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79, (4), 1143-1164, 2017.9
Largescale multiple testing with correlated test statistics arises frequently in much scientific research. Incorporating correlation information in approximating the false discovery proportion (FDP) has attracted increasing attention in recent years. When the covariance matrix of test statistics is known, Fan and his colleagues provided an accurate approximation of the FDP under arbitrary dependence structure and some sparsity assumption. However, the covariance matrix is often unknown in many applications and such dependence information must be estimated before approximating the FDP. The estimation accuracy can greatly affect the FDP approximation. In the current paper, we study theoretically the effect of unknown dependence on the testing procedure and establish a general framework such that the FDP can be well approximated. The effects of unknown dependence on approximating the FDP are in the
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@inproceedings{jianqing2017estimation,
  title={Estimation of the false discovery proportion with unknown dependence},
  author={Jianqing Fan, and Xu Han},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113809856677898},
  booktitle={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume={79},
  number={4},
  pages={1143-1164},
  year={2017},
}
Jianqing Fan, and Xu Han. Estimation of the false discovery proportion with unknown dependence. 2017. Vol. 79. In Journal of the Royal Statistical Society: Series B (Statistical Methodology). pp.1143-1164. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113809856677898.
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