Random effects regression mixtures for analyzing infant habituation

Derek S. Young, David R. Hunter

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.

Original languageEnglish
Pages (from-to)1421-1441
Number of pages21
JournalJournal of Applied Statistics
Issue number7
StatePublished - Jul 3 2015

Bibliographical note

Funding Information:
The authors are grateful to two anonymous referees for helpful comments during the preparation of this article. We also wish to thank Hoben Thomas from the Department of Psychology, Pennsylvania State University, Arnold Lohaus from the Department of Psychology, University of Marburg, and the German Research Foundation (DFG) for providing the infant data set.

Funding Information:
This work was supported by National Science Foundation Award [SES-0518772].

Publisher Copyright:
© 2015, © 2015 Taylor & Francis.


  • ECM algorithm
  • bootstrap
  • likelihood ratio test
  • switching regressions
  • trajectory data

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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