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A Comparison of Two Matrices for Testing Covariance Matrix in Unbalanced Linear Mixed Models

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  • BBarnabani M

    (Department of Statistics Informatics Applications, University of Florence, Italy)

Abstract

Despite the widespread use of mixed-effects regression model, available methods for testing the covariance matrix of random effects are quite limited. In these cases, because of complexity and difficulties coming from an analysis of multiple variance components, inference based on testing the equality of two positive semi definite matrices seems most appropriate.

Suggested Citation

  • BBarnabani M, 2017. "A Comparison of Two Matrices for Testing Covariance Matrix in Unbalanced Linear Mixed Models," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 3(3), pages 66-67, October.
    • Handle: RePEc:adp:jbboaj:v:3:y:2017:i:3:p:66-67
    DOI: 10.19080/BBOAJ.2017.03.555612
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