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Comparison of Independent Samples of High‐Dimensional Data by Pairwise Distance Measures
Author(s) -
Kropf Siegfried,
Lux Anke,
Eszlinger Markus,
Heuer Holger,
Smalla Kornelia
Publication year - 2007
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.200510262
Subject(s) - pairwise comparison , mathematics , statistics , similarity (geometry) , permutation (music) , parametric statistics , sample size determination , sample (material) , nonparametric statistics , cluster (spacecraft) , statistical hypothesis testing , computer science , artificial intelligence , image (mathematics) , physics , chemistry , chromatography , acoustics , programming language
Pairwise distance or association measures of sample elements are often used as a basis for hierarchical cluster analyses. They can also be used in tests for the comparison of pre‐defined subgroups of the total sample. Usually this is done with permutation tests. In this paper, we compare such a procedure with alternative tests for high‐dimensional data based on spherically distributed scores in simulation experiments and with real data. The tests based on the pairwise distance or similarity measures perform quite well in this comparison. As the number of possible permutations is small in very small samples, this might restrict the use of the test. Therefore, we propose an exact parametric small sample version of the test using randomly rotated samples. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)