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Multiple Testing with Minimal Assumptions
Author(s) -
Westfall Peter H.,
Troendle James F.
Publication year - 2008
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200710456
Subject(s) - resampling , multiple comparisons problem , multivariate statistics , computer science , false discovery rate , class (philosophy) , character (mathematics) , data mining , mathematics , statistics , algorithm , artificial intelligence , machine learning , biology , gene , genetics , geometry
Resampling‐based multiple testing methods that control the Familywise Error Rate in the strong sense are presented. It is shown that no assumptions whatsoever on the data‐generating process are required to obtain a reasonably powerful and flexible class of multiple testing procedures. Improvements are obtained with mild assumptions. The methods are applicable to gene expression data in particular, but more generally to any multivariate, multiple group data that may be character or numeric. The role of the disputed “subset pivotality” condition is clarified. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)