Estimation and Identification of Space-Time ARMAX Models in the Presence of Missing Data
提出一种结合当前空间方法与ARMAX模型参数化的方法，用于建模和拟合多元时空序列数据，其估计和识别过程能容忍数据缺失。
Abstract A method for modeling and fitting multivariate spatial time series data based on current spatial methodology coupled with the parameterization of the ARMAX model is presented. Because of the physical constraints imposed on multivariate data collection in both space and time, the estimation and identification procedures tolerate general patterns of missing or incomplete data.