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Inference on mean sub-vectors of two multivariate normal populations with unequal covariance matrices

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  • Gamage, Jinadasa
  • Mathew, Thomas

Abstract

The problem of testing the equality of sub-vectors of two multivariate normal mean vectors is addressed when the complementary sub-vectors are known to be equal, and the two populations have unequal covariance matrices. A test procedure is derived using the multivariate Satterthwaite approximation. The approximation is developed in such a way that the test satisfies a natural invariance condition. Accuracy of the approximation is numerically investigated, and the result is illustrated with an example.

Suggested Citation

  • Gamage, Jinadasa & Mathew, Thomas, 2008. "Inference on mean sub-vectors of two multivariate normal populations with unequal covariance matrices," Statistics & Probability Letters, Elsevier, vol. 78(4), pages 420-425, March.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:4:p:420-425
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    References listed on IDEAS

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    1. Krishnamoorthy, K. & Yu, Jianqi, 2004. "Modified Nel and Van der Merwe test for the multivariate Behrens-Fisher problem," Statistics & Probability Letters, Elsevier, vol. 66(2), pages 161-169, January.
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    Cited by:

    1. Tiefeng Ma & Shuangzhe Liu, 2013. "Estimation of order-restricted means of two normal populations under the LINEX loss function," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(3), pages 409-425, April.

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