Unbalanced repeated-measures models with structured covariance matrices

RI Jennrich, MD Schluchter - Biometrics, 1986 - JSTOR
RI Jennrich, MD Schluchter
Biometrics, 1986JSTOR
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 …
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.
JSTOR