  In large scale multiple testing problems, a two-class empirical Bayes
approach can be used to control the false discovery rate (Fdr) for the entire
array of hypotheses under study. A sample splitting step is incorporated to
modify that approach where one part of the data is used for model fitting and
the other part for detecting the significant cases by a screening technique
featuring the empirical Bayes mode of Fdr control. Cases with high detection
frequency across repeated random sample splits are considered true discoveries.
A critical detection frequency is set to control the overall false discovery
rate. The proposed method helps to balance out unwanted sources of variation
and addresses potential statistical overfitting of the core empirical model by
cross-validation through resampling. Further, concurrent detection frequencies
are used to provide visual tools to explore the inter-relationship between
significant cases. The methodology is illustrated using a microarray data set,
RNA-sequencing data set, and several simulation studies. A power analysis is
presented to understand the efficiency of the proposed method.
