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纵向数据一阶条件自回归模型的 EM 拟合AI 解读

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…

用 EM 算法拟合纵向数据的一阶条件自回归模型AI 解读

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…