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

Anuradha Roy and Ricardo Leiva
Additional contact information
Anuradha Roy: The University of Texas at San Antonio
Ricardo Leiva: F.C.E., Universidad Nacional de Cuyo
Authors registered in the RePEc Author Service: Avik Chakrabarti ()

No 37, Working Papers from College of Business, University of Texas at San Antonio

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.

Keywords: Kronecker product covariance structure; maximum likelihood estimates; equicorrelated partitioned matrix; three-level multivariate data. (search for similar items in EconPapers)
JEL-codes: C30 (search for similar items in EconPapers)
View list of references

Downloads: (external link)
http://business.utsa.edu/wps/MSS/0037MSS-253-2008.pdf Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:tsa:wpaper:0037

Access Statistics for this paper

More papers in Working Papers from College of Business, University of Texas at San Antonio
Contact information at EDIRC.
Series data maintained by Elsa Herrera ().