Unbalanced Repeated-Measures Models with Structured Covariance Matrices

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ID: 294622
1986
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Abstract
The question of how to analyze unbalanced or incomplete repeated-measures data is a common problem facing analysts. We address this problem through maximum likelihood analysis using a general linear model for expected responses and arbitrary structural models for the within-subject covariances. Models that can be fit include standard univariate and multivariate models with incomplete data, random-effects models, and models with time-series and factor-analytic error structures. We describe Newton-Raphson and Fisher scoring algorithms for computing maximum likelihood estimates, and generalized EM algorithms for computing restricted and unrestricted maximum likelihood estimates. An example fitting several models to a set of growth data is included.
Reference Key
openalex_W2064279221 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Robert I. Jennrich, Mark Schluchter
Journal biometrics
Year 1986
DOI
10.2307/2530695
URL
https://doi.org/10.2307/2530695
Keywords Keywords not found

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