Spatially Varying Auto-Regressive Models for Prediction of New Human Immunodeficiency Virus Diagnoses
针对空间数据丰富但时间点少的HIV公开数据，提出了一类结合条件自回归空间相关结构的空间变系数自回归模型，用于预测新HIV诊断率，在佛罗里达、加利福尼亚和新英格兰地区的应用中表现优于其他线性混合模型。
In demand of predicting new HIV diagnosis rates based on publicly available HIV data that is abundant in space but has few points in time, we propose a class of spatially varying autoregressive (SVAR) models compounded with conditional autoregressive (CAR) spatial correlation structures. We then propose to use the copula approach and a flexible CAR formulation to model the dependence between adjacent counties. These models allow for spatial and temporal correlation as well as space-time interactions and are naturally suitable for predicting HIV cases and other spatio-temporal disease data that feature a similar data structure. We apply the proposed models to HIV data over Florida, California and New England states and compare them to a range of linear mixed models that have been recently popular for modeling spatio-temporal disease data. The results show that for such data our proposed models outperform the others in terms of prediction.