Random-effects models for longitudinal data.

  title={Random-effects models for longitudinal data.},
  author={Nan M. Laird and James H. Ware},
  volume={38 4},
Models for the analysis of longitudinal data must recognize the relationship between serial observations on the same unit. Multivariate models with general covariance structure are often difficult to apply to highly unbalanced data, whereas two-stage random-effects models can be used easily. In two-stage models, the probability distributions for the response vectors of different individuals belong to a single family, but some random-effects parameters vary across individuals, with a… CONTINUE READING
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