An EM Algorithm Fitting First-Order Conditional Autoregressive Models to Longitudinal Data
提出一种EM算法，用于拟合状态空间形式的纵向回归模型，该模型处理连续响应与滞后响应、时变及非时变协变量的关系，并处理缺失数据和测量误差，成功应用于158名儿童6年肺功能测量数据。
An EM algorithm fits a state-space formulation of the longitudinal regression model in which a continuous response depends on the lagged response and both time-dependent and time-independent covariates. The baseline response depends only on covariates. The model handles both missing data and Gaussian measurement error on both response and continuous covariates. The E step uses the Kalman filter and associated filtering algorithms to update the unknown true response and predictor series for the observed data. The M step uses standard closed-form Gaussian results. Standard errors come from the supplemented EM (SEM) algorithm. The model accurately fits 6 years of pulmonary function measurements on 158 children with many missing observations.