Mixture models for clustering multilevel growth trajectories

S.K. Ng and G.J. McLachlan

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 43-51

Abstract: Mixture model-based methods assuming independence may not be valid for clustering growth trajectories arising from multilevel studies because longitudinal data collected from the same unit are often correlated. A mixture of mixed effects models is considered to capture the correlation using multilevel and multivariate random effects. Furthermore, the mixing proportions are allowed to depend on covariates. The additional information is thus incorporated into the mixture model to adjust for individual probabilities of membership of the components. The proposed method is illustrated using simulated and real multilevel growth trajectory data sets from various scientific fields.

Keywords: Mixture models; Random effects; Multilevel growth trajectories; EM algorithm (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:43-51

DOI: 10.1016/j.csda.2012.12.007

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