TY - JOUR
T1 - Model-based clustering for longitudinal data
AU - De la Cruz-Mesía, Rolando
AU - Quintana, Fernando A.
AU - Marshall, Guillermo
PY - 2008/1/1
Y1 - 2008/1/1
N2 - A model-based clustering method is proposed for clustering individuals on the basis of measurements taken over time. Data variability is taken into account through non-linear hierarchical models leading to a mixture of hierarchical models. We study both frequentist and Bayesian estimation procedures. From a classical viewpoint, we discuss maximum likelihood estimation of this family of models through the EM algorithm. From a Bayesian standpoint, we develop appropriate Markov chain Monte Carlo (MCMC) sampling schemes for the exploration of target posterior distribution of parameters. The methods are illustrated with the identification of hormone trajectories that are likely to lead to adverse pregnancy outcomes in a group of pregnant women.
AB - A model-based clustering method is proposed for clustering individuals on the basis of measurements taken over time. Data variability is taken into account through non-linear hierarchical models leading to a mixture of hierarchical models. We study both frequentist and Bayesian estimation procedures. From a classical viewpoint, we discuss maximum likelihood estimation of this family of models through the EM algorithm. From a Bayesian standpoint, we develop appropriate Markov chain Monte Carlo (MCMC) sampling schemes for the exploration of target posterior distribution of parameters. The methods are illustrated with the identification of hormone trajectories that are likely to lead to adverse pregnancy outcomes in a group of pregnant women.
KW - Cluster analysis
KW - EM algorithm
KW - Markov chain Monte Carlo
KW - Mixture model
KW - Non-linear models
KW - Random effects
UR - http://www.scopus.com/inward/record.url?scp=35548932932&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2007.04.005
DO - 10.1016/j.csda.2007.04.005
M3 - Article
AN - SCOPUS:35548932932
SN - 0167-9473
VL - 52
SP - 1441
EP - 1457
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
IS - 3
ER -