Autoregressive Model With Spatial Dependence and Missing Data
研究了一个误差项存在空间相关且数据有缺失的自回归模型，用逻辑回归描述缺失机制，提出了加权最小二乘和加权极大似然估计量，并用于北京PM2.5数据分析。
We study herein an autoregressive model with spatially correlated error terms and missing data. A logistic regression model with completely observed covariates is used to model the missingness mechanism. An autoregressive model is used to accommodate time series dependence, and a spatial error model is used to capture spatial dependence. To estimate the model, a weighted least squares estimator is developed for the temporal component, and a weighted maximum likelihood estimator is developed for the spatial component. The asymptotic properties for both estimators are investigated. The finite sample performance is assessed through extensive simulation studies. A real data example about Beijing’s PM2.5 level data is illustrated.