Bayesian Spatial Prediction of Random Space-Time Fields With Application to Mapping PM 2.5 Exposure
随机时空场的贝叶斯空间预测及 PM2.5 暴露制图应用
This article presents a multivariate spatial prediction methodology in a Bayesian framework. The method is especially suited for use in environmetrics, where vector-valued responses are observed at a small set of ambient monitoring stations “(gauged sites)” at successive time points. However, the stations may have varying start-up times so that the data have a “staircase” pattern (“monotone” pattern in the terminology of Rubin and Shaffer). The lowest step corresponds to the…