  This research deals with massive multiple hypothesis testing. First regarding
multiple tests as an estimation problem under a proper population model, an
error measurement called Erroneous Rejection Ratio (ERR) is introduced and
related to the False Discovery Rate (FDR). ERR is an error measurement similar
in spirit to FDR, and it greatly simplifies the analytical study of error
properties of multiple test procedures. Next an improved estimator of the
proportion of true null hypotheses and a data adaptive significance threshold
criterion are developed. Some asymptotic error properties of the significant
threshold criterion is established in terms of ERR under distributional
assumptions widely satisfied in recent applications. A simulation study
provides clear evidence that the proposed estimator of the proportion of true
null hypotheses outperforms the existing estimators of this important parameter
in massive multiple tests. Both analytical and simulation studies indicate that
the proposed significance threshold criterion can provide a reasonable balance
between the amounts of false positive and false negative errors, thereby
complementing and extending the various FDR control procedures. S-plus/R code
is available from the author upon request.
