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Search for relevant sets of variables in a high‐dimensional setup keeping the familywise error rate
Author(s) -
Läuter Jürgen,
Glimm Ekkehard,
Eszlinger Markus
Publication year - 2005
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2005.00290.x
Subject(s) - multivariate statistics , parametric statistics , false discovery rate , relation (database) , multivariate analysis , mathematics , multiple comparisons problem , computer science , multivariate normal distribution , statistics , data mining , algorithm , biology , gene , biochemistry
Two multiple procedures for the detection of relevant sets of variables in a high‐dimensional problem are suggested. Multivariate tests for significance are combined with the search for interpretable multivariate structures. Thus, groups of highly correlated variables are investigated. The emphasis lies on managing the huge number of possible subsets, for example, in gene expression analysis. The first procedure is based on parametric spherical tests and an order relation of the subsets. The second procedure is a non‐parametric method utilizing Westfall–Young principles.