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Checking identifiability of covariance parameters in linear mixed effects models

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  • Wei Wang

Abstract

To build a linear mixed effects model, one needs to specify the random effects and often the associated parametrized covariance matrix structure. Inappropriate specification of the structures can result in the covariance parameters of the model not identifiable. Non-identifiability can result in extraordinary wide confidence intervals, and unreliable parameter inference. Sometimes software produces implication of model non-identifiability, but not always. In the simulation of fitting non-identifiable models we tried, about half of the times the software output did not look abnormal. We derive necessary and sufficient conditions of covariance parameters identifiability which does not require any prior model fitting. The results are easy to implement and are applicable to commonly used covariance matrix structures.

Suggested Citation

  • Wei Wang, 2017. "Checking identifiability of covariance parameters in linear mixed effects models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(11), pages 1938-1946, August.
  • Handle: RePEc:taf:japsta:v:44:y:2017:i:11:p:1938-1946
    DOI: 10.1080/02664763.2016.1238050
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    References listed on IDEAS

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    1. Wei Wang, 2014. "Linear mixed function-on-function regression models," Biometrics, The International Biometric Society, vol. 70(4), pages 794-801, December.
    2. Daniel F. McCaffrey & J. R. Lockwood & Daniel Koretz & Thomas A. Louis & Laura Hamilton, 2004. "Models for Value-Added Modeling of Teacher Effects," Journal of Educational and Behavioral Statistics, , vol. 29(1), pages 67-101, March.
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