Mixture models for clustering multilevel growth trajectories

Ng S.K. and McLachlan G.J. (2014) Mixture models for clustering multilevel growth trajectories. Computational Statistics and Data Analysis, 71 43-51. doi:10.1016/j.csda.2012.12.007


Author Ng S.K.
McLachlan G.J.
Title Mixture models for clustering multilevel growth trajectories
Journal name Computational Statistics and Data Analysis   Check publisher's open access policy
ISSN 0167-9473
1872-7352
Publication date 2014-03-01
Year available 2012
Sub-type Article (original research)
DOI 10.1016/j.csda.2012.12.007
Volume 71
Start page 43
End page 51
Total pages 9
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
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.
Keyword EM algorithm
Mixture models
Multilevel growth trajectories
Random effects
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Available online ahead of print 20 December 2012.

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2015 Collection
 
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