This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 634
Type: Contributed
Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #306440
Title: Estimating and Testing the Blocked Compound Symmetry Covariance Structure for Doubly Multivariate Data
Author(s): Anuradha Roy*+ and Ricardo Leiva
Companies: The University of Texas at San Antonio and Universidad Nacional de Cuyo
Address: Department of Management Science and Statistics, San Antonio, TX, 78249,
Keywords: Blocked compound symmetry ; Maximum likelihood estimates ; Doubly multivariate data
Abstract:

We develop an approach for estimating and testing the blocked compound symmetry covariance structure for doubly multivariate data. Testing of such parsimonious covariance structure is potentially important for doubly multivariate data, where computation of unstructured variance covariance matrix is practically impossible for small sample size cases. The test is implemented with a real data set, where number of sites is 2 and number of response variables is 3. For this data set the unstructured variance-covariance matrix is (6 x 6)- dimensional, and therefore the number of unknown parameters in the unstructured variance-covariance matrix is 21; whereas the number of unknown parameters in the blocked compound symmetry covariance structure is only 12. Using our method we fail to reject the null hypothesis that the covariance structure is of the BCS with p-value 0.5786.


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