Power-enhanced multiple decision functions controlling family-wise error and false discovery rates

Details Power-enhanced multiple decision functions controlling family-wise error and false discovery rates Power-enhanced multiple decision functions controlling family-wise error and false discovery rates

2009-08-12

arxiv.org/abs/0908.1767v3

Annals of Statistics 2011, Vol. 39, No. 1, 556-583

Mathematics

Statistics Theory

Published in at http://dx.doi.org/10.1214/10-AOS844 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of

Scientific paper

10.1214/10-AOS844

Improved procedures, in terms of smaller missed discovery rates (MDR), for performing multiple hypotheses testing with weak and strong control of the family-wise error rate (FWER) or the false discovery rate (FDR) are developed and studied. The improvement over existing procedures such as the \v{S}id\'ak procedure for FWER control and the Benjamini--Hochberg (BH) procedure for FDR control is achieved by exploiting possible differences in the powers of the individual tests. Results signal the need to take into account the powers of the individual tests and to have multiple hypotheses decision functions which are not limited to simply using the individual $p$-values, as is the case, for example, with the \v{S}id\'ak, Bonferroni, or BH procedures. They also enhance understanding of the role of the powers of individual tests, or more precisely the receiver operating characteristic (ROC) functions of decision processes, in the search for better multiple hypotheses testing procedures. A decision-theoretic framework is utilized, and through auxiliary randomizers the procedures could be used with discrete or mixed-type data or with rank-based nonparametric tests. This is in contrast to existing $p$-value based procedures whose theoretical validity is contingent on each of these $p$-value statistics being stochastically equal to or greater than a standard uniform variable under the null hypothesis. Proposed procedures are relevant in the analysis of high-dimensional "large $M$, small $n$" data sets arising in the natural, physical, medical, economic and social sciences, whose generation and creation is accelerated by advances in high-throughput technology, notably, but not limited to, microarray technology.

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