  Multiple hypothesis testing has been widely applied to problems dealing with
high-dimensional data, e.g., selecting significant variables and controlling
the selection error rate. The most prevailing measure of error rate used in the
multiple hypothesis testing is the false discovery rate (FDR). In recent years,
local false discovery rate (fdr) has drawn much attention, due to its advantage
of accessing the confidence of individual hypothesis. However, most methods
estimate fdr through p-values or statistics with known null distributions,
which are sometimes not available or reliable. Adopting the innovative
methodology of competition-based procedures, e.g., knockoff filter, this paper
proposes a new approach, named TDfdr, to local false discovery rate estimation,
which is free of the p-values or known null distributions. Simulation results
demonstrate that TDfdr can accurately estimate the fdr with two
competition-based procedures. In real data analysis, the power of TDfdr on
variable selection is verified on two biological datasets.
