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

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

Author Info

Anuradha Roy (The University of Texas at San Antonio)
Ricardo Leiva (F.C.E., Universidad Nacional de Cuyo)

Additional information is available for the following registered author(s):

Avik Chakrabarti

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.

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Paper provided by College of Business, University of Texas at San Antonio in its series Working Papers with number 0037.

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Length: 13 pages
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Handle: RePEc:tsa:wpaper:0037

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

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