Jennrich R I, Schluchter M D
Biometrics. 1986 Dec;42(4):805-20.
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.
如何分析不平衡或不完整的重复测量数据这一问题是分析人员面临的常见问题。我们通过使用期望响应的一般线性模型和受试者内协方差的任意结构模型进行最大似然分析来解决这个问题。可以拟合的模型包括具有不完整数据的标准单变量和多变量模型、随机效应模型以及具有时间序列和因子分析误差结构的模型。我们描述了用于计算最大似然估计的牛顿 - 拉弗森和费希尔评分算法，以及用于计算受限和无约束最大似然估计的广义期望最大化算法。文中包含了一个对一组生长数据拟合多个模型的示例。
