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Two Sample Tests for High Dimensional Covariance Matrices

Song Chen

MPRA Paper from University Library of Munich, Germany

Abstract: We propose two tests for the equality of covariance matrices between two high-dimensional populations. One test is on the whole variance-covariance matrices, and the other is on offdiagonal sub-matrices which define the covariance between two non-overlapping segments of the high-dimensional random vectors. The tests are applicable (i) when the data dimension is much larger than the sample sizes, namely the “large p, small n” situations and (ii) without assuming parametric distributions for the two populations. These two aspects surpass the capability of the conventional likelihood ratio test. The proposed tests can be used to test on covariances associated with gene ontology terms.

Keywords: High dimensional covariance; Large p small n; Likelihood ratio test; Testing for Gene-sets. (search for similar items in EconPapers)
JEL-codes: C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 G0 G1 G2 G3 (search for similar items in EconPapers)
Date: 2012-05-04
New Economics Papers: this item is included in nep-ecm
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https://mpra.ub.uni-muenchen.de/46026/1/MPRA_paper_46026.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/46278/1/MPRA_paper_46026.pdf revised version (application/pdf)

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