Abstract
As discussed in Chapter 8, the SAS procedure PROC MIXED allows the user to fit general linear mixed models, with a large variety of possible covariance structures. Under the linear mixed model (3.11), the data vector Y i for the ith subject is assumed to be normally distributed with mean vector X i ß and covariance matrix of the form \( V_i = Z_i DZ_i^\prime + \sigma ^2 I_{n_i } + \tau ^2 H_i \) . Hence, fitting linear mixed models implies that an appropriate mean structure as well as covariance structure needs to be specified. As shown in Figure 9.1, they are not independent of each other.
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© 2000 Springer-Verlag New York, Inc.
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(2000). General Guidelines for Model Building. In: Linear Mixed Models for Longitudinal Data. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-22775-7_9
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DOI: https://doi.org/10.1007/978-0-387-22775-7_9
Publisher Name: Springer, New York, NY
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