A review of modern multiple hypothesis testing, with particular attention to the false discovery proportion.

In the last decade a growing amount of statistical research has been devoted to multiple testing, motivated by a variety of applications in medicine, bioinformatics, genomics, brain imaging, etc. Research in this area is focused on developing powerful procedures even when the number of tests is very large. This paper attempts to review research in modern multiple hypothesis testing with particular attention to the false discovery proportion, loosely defined as the number of false rejections divided by the number of rejections. We review the main ideas, stepwise and augmentation procedures; and resampling based testing. We also discuss the problem of dependence among the test statistics. Simulations make a comparison between the procedures and with Bayesian methods. We illustrate the procedures in applications in DNA microarray data analysis. Finally, few possibilities for further research are highlighted.