Estimation of Time Series Models in the Presence of Missing Data
提出了一种在缺失数据情况下估计离散时间序列模型的方法，通过模拟研究评估其在简单模型中的表现，并应用于含有缺失观测的污染水平时间序列。
Abstract A method is proposed for the estimation of models for discrete time series in the presence of missing data. Some justification is given for the use of this method over alternatives; the choice of estimator is likely to be governed by the pattern of missing data, the nature of the time series model, and computational considerations. The method's performance in estimating simple models is studied by simulations, and it is applied to a time series of pollution levels containing some missing observations.