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A new proposal for a principal component‐based test for high‐dimensional data applied to the analysis of PhyloChip data
Author(s) -
Ding GuoChun,
Smalla Kornelia,
Heuer Holger,
Kropf Siegfried
Publication year - 2012
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.201000164
Subject(s) - principal component analysis , statistics , mathematics , test statistic , multivariate statistics , parametric statistics , normality test , nonparametric statistics , statistic , outlier , statistical hypothesis testing , data mining , computer science
A modification of the principal component test is presented. It uses a weighted combination of the sums of squares for different principal components and is thus more powerful in high‐dimensional settings with small sample sizes. Under usual normality assumptions, a rotation test is proposed which enables an exact conditional parametric test. The procedure is demonstrated with microarray data for the bacterial composition in the rhizosphere of different potato cultivars. In simulation studies, the power of the proposed statistic is compared with the competing multivariate parametric tests.