|
We provide a data-driven method for modelling the conditional, within-subject
covariance matrix arising in linear mixed models (Laird and Ware,
1982). Given an agreed structure for the between-subject covariance matrix
we use a regression equation approach to model the within-subject covariance matrix.
Using an EM algorithm we estimate all of the parameters in the model simultaneously
and obtain analytical expressions for the standard errors. By re-analyzing
Kenward's (1987) cattle data, we compare our new model
with classical menu-selection–based modelling techniques, demonstrating
its superiority using the Bayesian Information Criterion. We also conduct a
simulation study, which confirms our observational findings. The paper extends our
previous covariance modeling work (Pan and MacKenzie, 2003, 2006)
to the conditional covariance space of the linear mixed model (LMM).
|
