Mixture models for clustering multilevel growth trajectories
Author(s)
Ng, SK
McLachlan, GJ
Griffith University Author(s)
Year published
2014
Metadata
Show full item recordAbstract
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 ...
View more >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.
View less >
View more >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.
View less >
Journal Title
Computational Statistics and Data Analysis
Volume
71
Subject
Statistics
Theory of computation
Econometrics