We develop a new class of distribution free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data driven threshold along the ranking to control the FDR. For more information, see the website below and the accompanying paper: Du et al. (2020), "False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation", <arXiv:2002.11992>.
Package details |
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Author | Lilun Du [aut, cre], Xu Guo [ctb], Wenguang Sun [ctb], Changliang Zou [ctb] |
Maintainer | Lilun Du <dulilun@ust.hk> |
License | GPL (>= 2) |
Version | 1.0.0 |
Package repository | View on CRAN |
Installation |
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