High-dimensional inference on covariance structures via the extended cross-data-matrix methodology

Kazuyoshi Yata; Makoto Aoshima

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arXiv · 1503.06492

High-dimensional inference on covariance structures via the extended cross-data-matrix methodology

Kazuyoshi Yata·Makoto Aoshima

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Abstract

In this paper, we consider testing the correlation coefficient matrix between two subsets of high-dimensional variables. We produce a test statistic by using the extended cross-data-matrix (ECDM) methodology and show the unbiasedness of ECDM estimator. We also show that the ECDM estimator has the consistency property and the asymptotic normality in high-dimensional settings. We propose a test procedure by the ECDM estimator and evaluate its asymptotic size and power theoretically and numerically. We give several applications of the ECDM estimator. Finally, we demonstrate how the test procedure performs in actual data analyses by using a microarray data set.

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Kazuyoshi Yata, Makoto Aoshima. 2015-03-22. High-dimensional inference on covariance structures via the extended cross-data-matrix methodology. https://arxiv.org/abs/1503.06492

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