Alfó Marco, Nieddu Luciano, Vicari Donatella
Dipartimento di Statistica, Sapienza - Università di Roma, Italy.
Biom J. 2009 Feb;51(1):84-97. doi: 10.1002/bimj.200810494.
A vast literature has recently been concerned with the analysis of variation in disease counts recorded across geographical areas with the aim of detecting clusters of regions with homogeneous behavior. Most of the proposed modeling approaches have been discussed for the univariate case and only very recently spatial models have been extended to predict more than one outcome simultaneously. In this paper we extend the standard finite mixture models to the analysis of multiple, spatially correlated, counts. Dependence among outcomes is modeled using a set of correlated random effects and estimation is carried out by numerical integration through an EM algorithm without assuming any specific parametric distribution for the random effects. The spatial structure is captured by the use of a Gibbs representation for the prior probabilities of component membership through a Strauss-like model. The proposed model is illustrated using real data.
最近，大量文献关注于分析跨地理区域记录的疾病计数变化，目的是检测行为同质的区域集群。大多数提出的建模方法都是针对单变量情况进行讨论的，直到最近空间模型才被扩展到同时预测多个结果。在本文中，我们将标准有限混合模型扩展到对多个空间相关计数的分析。通过一组相关随机效应来对结果之间的依赖性进行建模，并通过EM算法进行数值积分来进行估计，而无需对随机效应假设任何特定的参数分布。通过类似施特劳斯模型的吉布斯表示来捕捉成分隶属度的先验概率，从而获得空间结构。使用实际数据对所提出的模型进行了说明。
