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Testing of a Structures Covariance Matrix for Three-Level Repeated Measures Data

Author

Listed:
  • Anuradha Roy

    (The University of Texas at San Antonio)

  • Ricardo Leiva

    (F.C.E., Universidad Nacional de Cuyo)

Abstract

This paper considers the problem of estimating, and testing for, a Kronecker product covariance structure of three-level (multiple time points (p), multiple sites (u), and multiple response variables (q)) multivariate data. Testing of such covariance structures is potentially important when not enough samples are available to estimate the unstructures variance-covariance matrix. This hypothesis procedure not only can test the hypothesis on three-level multivariate data, but also can test the hypotheses on two-level multivariate data as special cases. We provide the maximum likelihood estimates of the unknown population parameters. The test is implemented with a real data set.

Suggested Citation

  • Anuradha Roy & Ricardo Leiva, "undated". "Testing of a Structures Covariance Matrix for Three-Level Repeated Measures Data," Working Papers 0037, College of Business, University of Texas at San Antonio.
  • Handle: RePEc:tsa:wpaper:0037
    as

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    File URL: http://business.utsa.edu/wps/MSS/0037MSS-253-2008.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Kronecker product covariance structure; maximum likelihood estimates; equicorrelated partitioned matrix; three-level multivariate data.;

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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