Estimation, Prediction, and Interpolation for ARIMA Models with Missing Data
本文展示了如何定义并高效计算含缺失观测的ARIMA模型的边际似然，并利用修正的卡尔曼滤波进行预测和插补，同时给出估计的均方误差。
Abstract We show how to define and then compute efficiently the marginal likelihood of an ARIMA model with missing observations. The computation is carried out by using the univariate version of the modified Kalman filter introduced by Ansley and Kohn (1985a), which allows a partially diffuse initial state vector. We also show how to predict and interpolate missing observations and obtain the mean squared error of the estimate.