Projection tests for high-dimensional spiked covariance matrices
Wenwen Guo and
Journal of Multivariate Analysis, 2019, vol. 169, issue C, 21-32
Testing the existence of low-dimensional perturbations or signals is very important, e.g., in factor analysis and signal processing. This paper aims to develop new tests for high-dimensional spiked covariance matrices based on a projection approach. The asymptotic distribution of the proposed tests is obtained under regularity conditions. We further explore a power enhancement technique under covariance matrix sparsity. The finite-sample enhanced power performance of the proposed tests is shown through simulations. A microarray dataset is used for illustration purposes.
Keywords: Large p small n; Power enhancement technique; Projection; Spiked covariance matrix (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:169:y:2019:i:c:p:21-32
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