An adaptive significance threshold criterion for massive multiple hypotheses testing

Details An adaptive significance threshold criterion for massive multiple hypotheses testing An adaptive significance threshold criterion for massive multiple hypotheses testing

2006-10-27

arxiv.org/abs/math/0610845v1

IMS Lecture Notes--Monograph Series 2006, Vol. 49, 51-76

Mathematics

Statistics Theory

Published at http://dx.doi.org/10.1214/074921706000000392 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/

Scientific paper

10.1214/074921706000000392

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.

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