论文标题
使用基于图的优化改进了Areal单位计数数据的推断
Improved inference for areal unit count data using graph-based optimisation
论文作者
论文摘要
Areal单位计数数据中的空间相关性数据通常由一组随机效应建模，这些效应被分配为有条件的自回旋（CAR）之前的分布。该模型隐含的空间相关结构取决于二进制邻域矩阵，如果它们的区域具有共同的边界，则假定其两种随机效应是部分自相关的，否则在有条件地独立。本文提出了一种基于图形的新型优化算法，用于通过将面积单元视为图和邻居关系作为边缘集的数据来估算数据中的邻域矩阵。与使用边界共享规则相比，我们的方法的优势是通过模拟的全面证明，在该方法在2011年至2017年之间的大格拉斯哥和克莱德卫生委员会的新呼吸道疾病监视研究中。
Spatial correlation in areal unit count data is typically modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. The spatial correlation structure implied by this model depends on a binary neighbourhood matrix, where two random effects are assumed to be partially autocorrelated if their areal units share a common border, and are conditionally independent otherwise. This paper proposes a novel graph-based optimisation algorithm for estimating the neighbourhood matrix from the data, by viewing the areal units as the vertices of the graph and the neighbour relations as the set of edges. The superiority of our methodology compared to using the border sharing rule is comprehensively evidenced by simulation, before the method is applied to a new respiratory disease surveillance study in the Greater Glasgow and Clyde Health board in Scotland between 2011 and 2017.
