Pan, Jianxin and von Rosen, Dietrich (2006) Modelling Heterogeneous Covariances in the Growth Curve Models. [MIMS Preprint]
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Abstract
The growth curve models (GCM) are widely used in longitudinal studies and repeated measures. Most existing approaches for statistical inference in the GCM assume a specific structure on the within-subject covariances, for example, compound symmetry, AR(1) and unstructured covariances. The specification, however, may select a suboptimal or even wrong model, which in turn may affect the estimates of regression coef- ¯cients and/or bias standard errors of the estimates. Accordingly, statistical inferences of the models may be severely influenced by misspecification of covariance structures. Within the framework of the GCM in this paper we propose a data-driven approach for modelling the within-subject covariance structures, investigate the effects of misspecification of co- variance structures on statistical inferences, and study the heterogeneity of covariances between different treatment groups.
| Item Type: | MIMS Preprint |
|---|---|
| Uncontrolled Keywords: | Growth curve models, heterogeneity of covariances, maximum likelihood estimation, misspecification of covariance structures. |
| Subjects: | MSC 2010, the AMS's Mathematics Subject Classification > 62 Statistics |
| Depositing User: | Dr Peter Neal |
| Date Deposited: | 12 Apr 2006 |
| Last Modified: | 08 Nov 2017 18:18 |
| URI: | https://eprints.maths.manchester.ac.uk/id/eprint/213 |
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