Correlation tests for high-dimensional data using extended cross-data-matrix methodology

Authors:
Kazuyoshi Yata;Makoto Aoshima
Affiliations:
-;-
Venue:
Journal of Multivariate Analysis
Year:
2013

Citing 3
Cited 0

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Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix

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Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations

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Abstract

In this paper, we consider tests of correlation when the sample size is much lower than the dimension. We propose a new estimation methodology called the extended cross-data-matrix methodology. By applying the method, we give a new test statistic for high-dimensional correlations. We show that the test statistic is asymptotically normal when p-~ and n-~. We propose a test procedure along with sample size determination to ensure both prespecified size and power for testing high-dimensional correlations. We further develop a multiple testing procedure to control both family wise error rate and power. Finally, we demonstrate how the test procedures perform in actual data analyses by using two microarray data sets.