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Projection tests for high-dimensional spiked covariance matrices

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  • Guo, Wenwen
  • Cui, Hengjian

Abstract

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.

Suggested Citation

  • Guo, Wenwen & Cui, Hengjian, 2019. "Projection tests for high-dimensional spiked covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 21-32.
    • Handle: RePEc:eee:jmvana:v:169:y:2019:i:c:p:21-32
    DOI: 10.1016/j.jmva.2018.08.009
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    References listed on IDEAS

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    Cited by:

    1. Congran Yu & Hengjian Cui, 2025. "Feature screening via false discovery rate control for linear model with multivariate responses," Statistical Papers, Springer, vol. 66(2), pages 1-29, February.

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