Conditioning in the Growth Curve Model
研究了增长曲线模型中条件变量或协变量的先验约简，发现当协方差矩阵来自自回归过程且轮廓设计矩阵属于特定类别时，可减少所需协变量数量，并系统分析了约简程度与过程阶数的关系。
We consider a priori reduction of the number of conditioning variables or covariates in the growth curve model. Such reduction depends on the relation between the inverse covariance matrix and the profile design matrix. If the covariance matrix arises from an autoregressive process and the profile design matrix belongs to a certain class, the number of covariates required may be reduced. The extent of the reduction may be examined in a systematic fashion and depends on the order of the process. The random coefficients model is discussed briefly and an example is presented.