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Testing the equality of several covariance matrices with fewer observations than the dimension

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  • Srivastava, Muni S.
  • Yanagihara, Hirokazu
Abstract
For normally distributed data from the k populations with mxm covariance matrices [Sigma]1,...,[Sigma]k, we test the hypothesis H:[Sigma]1=...=[Sigma]k vs the alternative A[not equal to]H when the number of observations Ni, i=1,...,k from each population are less than or equal to the dimension m, Ni

Suggested Citation

  • Srivastava, Muni S. & Yanagihara, Hirokazu, 2010. "Testing the equality of several covariance matrices with fewer observations than the dimension," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1319-1329, July.
    • Handle: RePEc:eee:jmvana:v:101:y:2010:i:6:p:1319-1329
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    References listed on IDEAS

    as
    1. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
    2. Schott, James R., 2007. "A test for the equality of covariance matrices when the dimension is large relative to the sample sizes," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6535-6542, August.
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