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This research deals with massive multiple hypothesis testing. First regardingmultiple tests as an estimation problem under a proper population model, anerror measurement called Erroneous Rejection Ratio (ERR) is introduced andrelated to the False Discovery Rate (FDR). ERR is an error measurement similarin spirit to FDR, and it greatly simplifies the analytical study of errorproperties of multiple test procedures. Next an improved estimator of theproportion of true null hypotheses and a data adaptive significance thresholdcriterion are developed. Some asymptotic error properties of the significantthreshold criterion is established in terms of ERR under distributionalassumptions widely satisfied in recent applications. A simulation studyprovides clear evidence that the proposed estimator of the proportion of truenull hypotheses outperforms the existing estimators of this important parameterin massive multiple tests. Both analytical and simulation studies indicate thatthe proposed significance threshold criterion can provide a reasonable balancebetween the amounts of false positive and false negative errors, therebycomplementing and extending the various FDR control procedures. S-plus/R codeis available from the author upon request.

An adaptive significance threshold criterion for massive multiple hypotheses testing