论文标题
依赖于泊松随机变量的集合
Dependence on a collection of Poisson random variables
论文作者
论文摘要
我们提出了两种新颖的方法，通过在三个级别的分层模型中使用潜在变量来引入泊松计数之间的依赖性。感兴趣的随机变量的边际分布是泊松，具有严格的平稳性为特殊情况。顺序 - $ p $依赖性是为随机变量的时间顺序详细描述的，但是时空或时空依赖性也可以。描述了对模型的完整贝叶斯推断，并通过对墨西哥的孕产妇死亡率进行数字分析来说明模型的性能。还讨论了应对过度分散的扩展。
We propose two novel ways of introducing dependence among Poisson counts through the use of latent variables in a three levels hierarchical model. Marginal distributions of the random variables of interest are Poisson with strict stationarity as special case. Order--$p$ dependence is described in detail for a temporal sequence of random variables, however spatial or spatio-temporal dependencies are also possible. A full Bayesian inference of the models is described and performance of the models is illustrated with a numerical analysis of maternal mortality in Mexico. Extensions to cope with overdispersion are also discussed.
