期刊
ANNALS OF APPLIED STATISTICS
卷 3, 期 3, 页码 922-942出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/09-AOAS236
关键词
Total correlation; effective sample size; permutation tests; matrix normal distribution; row and column correlations
资金
- NSF [DMS-00-72360]
- National Institute of Health [8801 EB002784]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [0804324] Funding Source: National Science Foundation
Having observed an m x n matrix X whose rows are possibly correlated, we wish to test the hypothesis that the columns are independent of each other. Our motivation comes from microarray studies, where the rows of X record expression levels for in different genes, often highly correlated, while the columns represent n individual microarrays, presumably obtained independently. The presumption of independence underlies all the familiar permutation, cross-validation and bootstrap methods for microarray analysis, so it is important to know when independence fails. We develop nonparametric and normal-theory testing methods. The row and column correlations of X interact with each other in a way that complicates test procedures, essentially by reducing the accuracy of the relevant estimators.
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