A New Representation of Uniform-Block Matrix and Applications
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A covariance matrix with a special pattern (e.g., sparsity or block structure) is essential for conducting multivariate analysis on high-dimensional data. Recently, a block covariance or correlation pattern has been observed in various biological and biomedical studies, such as gene expression, proteomics, neuroimaging, exposome, and seed quality, among others. Specifically, this pattern partitions the population covariance matrix into uniform (i.e., equal variances and covariances) blocks. However, the unknown mathematical properties of matrices with this pattern limit the incorporation of this pre-determined covariance information into research. To address this gap, we propose a block Hadamard product representation that utilizes two lower-dimensional "coordinate" matrices and a pre-specific vector. This representation enables the explicit expressions of the square or power, determinant, inverse, eigendecomposition, canonical form, and the other matrix functions of the original larger-dimensional matrix on the basis of these "coordinate" matrices. By utilizing this representation, we construct null distributions of information test statistics for the population mean(s) in both single and multiple sample cases, which are extensions of Hotelling's T^2 and T_0^2, respectively.
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