Controlling the False Discovery Rate via symmetrized data aggregation based on SLOPE

Abstract

The symmetrized data aggregation (SDA) method has obvious advantages in controlling false discovery rate (FDR) under dependence, but it is affected by data sparsity level, signal amplitude and feature correlation structures. The FDR control becomes less accurate due to the additional estimation errors. In this paper, we expand the symmetrized data aggregation (SDA) filter by using sorted L-one penalized estimation (SLOPE) method, and in comparison with SDA, SDA-SLOPE method can estimate FDR more accurately and improve the true discovery rate (TDR). Through simulation study, it is found that SDA-SLOPE can adapt to the changes of data sparsity, signal amplitude and feature correlation, and the robustness and effectiveness of this method in FDR control are verified.