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Error control in tree structured hypothesis testing
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AI
DOI：10.1002/wics.1603.png)
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摘要
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This manuscript reviews some recent and popular error control methods for tree structured hypothesis testing. We review a common setting/definition for hypotheses arranged in a tree structure and we discuss two common Type I errors present in multiple testing: family wise error rates (FWERs) and false discovery rate (FDR). We also contrast these methods with a recent development designed to control the false selection rate (FSR). We discuss the algorithms used to implement these error controls and the strategies used to navigate tree structures in light of these errors. We highlight the assumptions necessary in these strategies, summarize the available R software packages to implement these approaches, and show them at work on an example.This article is categorized under:Data: Types and Structure > Image and Spatial DataApplications of Computational Statistics > Genomics/Proteomics/GeneticsData: Types and Structure > Microarrays
Keyword:
FDR
FWER
hierarchical testing
sequential testing
tree structure
期刊
W
IF:
5.4
论文数:
201
被引数:
5.1K
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