title: Incorporating the empirical null hypothesis into the Benjamini-Hochberg procedure.
abstract: For the problem of multiple testing, the Benjamini-Hochberg (B-H) procedure has become a very popular method in applications. We show how the B-H procedure can be interpreted as a test based on the spacings corresponding to the p-value distributions. This interpretation leads to the incorporation of the empirical null hypothesis, a term coined by Efron (2004). We develop a mixture modelling approach for the empirical null hypothesis for the B-H procedure and demonstrate some theoretical results regarding both finite-sample as well as asymptotic control of the false discovery rate. The methodology is illustrated with application to two high-throughput datasets as well as to simulated data.
authors: Penn State University, PA, USA.
authors: Debashis
authors: D
authors: Ghosh
mesh_terms: D003627:Data Interpretation, Statistical; D036262:Empirical Research; D005189:False Positive Reactions; D057166:High-Throughput Screening Assays / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D012107:Research Design