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Autoregressive Model With Spatial Dependence and Missing Data
Version 2 2020-06-08, 12:37
Version 1 2020-05-12, 08:52
journal contribution
posted on 2020-06-08, 12:37 authored by Jing Zhou, Jin Liu, Feifei Wang, Hansheng WangWe 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 PM level data is illustrated.