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A new proposal for a principal component-based test for high-dimensional data applied to the analysis of PhyloChip data

GND
102059845X
Affiliation
Julius Kühn-Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Epidemiology and Pathogen Diagnostics, Brunswick, Germany
Ding, Guo-Chun;
GND
1058967878
Affiliation
Julius Kühn-Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Epidemiology and Pathogen Diagnostics, Brunswick, Germany
Smalla, Kornelia;
GND
1058940058
Affiliation
Julius Kühn-Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Epidemiology and Pathogen Diagnostics, Brunswick, Germany
Heuer, Holger;
Affiliation
Institute for Biometry and Medical Informatics, Otto von Guericke University, Leipziger StraXe 44, 39120 Magdeburg, Germany
Kropf, Siegfried

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.

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