Open Access
May 2010 Strong approximations of level exceedences related to multiple hypothesis testing
Peter Hall, Qiying Wang
Bernoulli 16(2): 418-434 (May 2010). DOI: 10.3150/09-BEJ220


Particularly in genomics, but also in other fields, it has become commonplace to undertake highly multiple Student’s t-tests based on relatively small sample sizes. The literature on this topic is continually expanding, but the main approaches used to control the family-wise error rate and false discovery rate are still based on the assumption that the tests are independent. The independence condition is known to be false at the level of the joint distributions of the test statistics, but that does not necessarily mean, for the small significance levels involved in highly multiple hypothesis testing, that the assumption leads to major errors. In this paper, we give conditions under which the assumption of independence is valid. Specifically, we derive a strong approximation that closely links the level exceedences of a dependent “studentized process” to those of a process of independent random variables. Via this connection, it can be seen that in high-dimensional, low sample-size cases, provided the sample size diverges faster than the logarithm of the number of tests, the assumption of independent t-tests is often justified.


Download Citation

Peter Hall. Qiying Wang. "Strong approximations of level exceedences related to multiple hypothesis testing." Bernoulli 16 (2) 418 - 434, May 2010.


Published: May 2010
First available in Project Euclid: 25 May 2010

zbMATH: 1323.62049
MathSciNet: MR2668908
Digital Object Identifier: 10.3150/09-BEJ220

Keywords: False discovery rate , family-wise error rate , genomic data , large deviation probability , moving average , Poisson approximation , Student’s t-statistic , upper tail dependence , upper tail independence

Rights: Copyright © 2010 Bernoulli Society for Mathematical Statistics and Probability

Vol.16 • No. 2 • May 2010
Back to Top