Random effects regression mixtures for analyzing infant habituation

Derek S. Young, David R. Hunter

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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 (US)
Pages (from-to)1421-1441
Number of pages21
JournalJournal of Applied Statistics
Volume42
Issue number7
DOIs
StatePublished - Jul 3 2015

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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