Variance of the number of false discoveries
成果类型：
Article
署名作者：
Owen, AB
署名单位：
Stanford University
刊物名称：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
ISSN/ISSBN：
1369-7412
DOI：
10.1111/j.1467-9868.2005.00509.x
发表日期：
2005
页码：
411-426
关键词：
摘要：
In high throughput genomic work, a very large number d of hypotheses are tested based on n << d data samples. The large number of tests necessitates an adjustment for false discoveries in which a true null hypothesis was rejected. The expected number of false discoveries is easy to obtain. Dependences between the hypothesis tests greatly affect the variance of the number of false discoveries. Assuming that the tests are independent gives an inadequate variance formula. The paper presents a variance formula that takes account of the correlations between test statistics. That formula involves O(d(2)) correlations, and so a naive implementation has cost O(nd(2)). A method based on sampling pairs of tests allows the variance to be approximated at a cost that is independent of d.
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