Abstract
In Section 7.8.4, we discussed how Verbeke and Lesaffre (1996a, 1997b) extended the linear mixed model to cases where the random effects are not necessarily normally distributed. Their so-called heterogeneity model assumes the random effects to be sampled from a mixture of normal distributions rather than from just one single normal distribution. This not only extends the assumption about the random-effects distribution to a very broad class of distributions (unimodal as well as multimodal, symmetric as well as highly skewed; see Figure 7.5), it is also perfectly suitable for classification purposes, based on longitudinal profiles.
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© 2000 Springer-Verlag New York, Inc.
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(2000). The Heterogeneity Model. 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_12
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DOI: https://doi.org/10.1007/978-0-387-22775-7_12
Publisher Name: Springer, New York, NY
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