Farcomeni Alessio
Università di Roma La Sapienza, Roma, Italy.
Stat Methods Med Res. 2008 Aug;17(4):347-88. doi: 10.1177/0962280206079046. Epub 2007 Aug 14.
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.
在过去十年中，受医学、生物信息学、基因组学、脑成像等各种应用的推动，越来越多的统计研究致力于多重检验。该领域的研究专注于开发强大的方法，即使检验数量非常大。本文试图回顾现代多重假设检验的研究，特别关注错误发现比例，其大致定义为错误拒绝的数量除以拒绝的数量。我们回顾主要思想、逐步和增强程序以及基于重采样的检验。我们还讨论了检验统计量之间的相关性问题。通过模拟对这些程序与贝叶斯方法进行比较。我们在DNA微阵列数据分析的应用中展示这些程序。最后，强调了一些进一步研究的可能性。
