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More powerful tests for sparse high-dimensional covariances matrices

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  • Peng, Liuhua
  • Chen, Song Xi
  • Zhou, Wen

Abstract

This paper considers improving the power of tests for the identity and sphericity hypotheses regarding high dimensional covariance matrices. The power improvement is achieved by employing the banding estimator for the covariance matrices, which leads to significant reduction in the variance of the test statistics in high dimension. Theoretical justification and simulation experiments are provided to ensure the validity of the proposed tests. The tests are used to analyze a dataset from an acute lymphoblastic leukemia gene expression study for an illustration.

Suggested Citation

  • Peng, Liuhua & Chen, Song Xi & Zhou, Wen, 2016. "More powerful tests for sparse high-dimensional covariances matrices," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 124-143.
    • Handle: RePEc:eee:jmvana:v:149:y:2016:i:c:p:124-143
    DOI: 10.1016/j.jmva.2016.03.008
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    Cited by:

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