Model-based clustering for longitudinal data

Rolando De la Cruz-Mesía, Fernando A. Quintana, Guillermo Marshall

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1441-1457
Number of pages17
JournalComputational Statistics and Data Analysis
Volume52
Issue number3
DOIs
StatePublished - 1 Jan 2008

Keywords

  • Cluster analysis
  • EM algorithm
  • Markov chain Monte Carlo
  • Mixture model
  • Non-linear models
  • Random effects

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