论文标题
贝叶斯非参数多元空间混合混合效应模型，并适用于美国社区调查特别列表
Bayesian Nonparametric Multivariate Spatial Mixture Mixed Effects Models with Application to American Community Survey Special Tabulations
论文作者
论文摘要
利用多元空间依赖来提高使用美国社区调查数据和其他样本调查数据提高估计的精度，这是数据用户和联邦统计机构最近感兴趣的话题。一种策略是使用高斯观察模型和潜在的高斯过程模型，使用多元空间混合效应模型。在实践中，这对于广泛的列表非常有效。然而，在数据中地理位置和/或稀疏性之间表现出异质性的情况下，高斯假设可能是有问题的，并且导致表现不佳。为了解决这些情况，我们提出了一个多元分层的贝叶斯非参数混合效应空间混合模型，以提高模型的灵活性。集群数以数据驱动方式自动选择。通过模拟研究和激励对美国社区调查数据的特殊列表的应用来证明我们方法的有效性。
Leveraging multivariate spatial dependence to improve the precision of estimates using American Community Survey data and other sample survey data has been a topic of recent interest among data-users and federal statistical agencies. One strategy is to use a multivariate spatial mixed effects model with a Gaussian observation model and latent Gaussian process model. In practice, this works well for a wide range of tabulations. Nevertheless, in situations that exhibit heterogeneity among geographies and/or sparsity in the data, the Gaussian assumptions may be problematic and lead to underperformance. To remedy these situations, we propose a multivariate hierarchical Bayesian nonparametric mixed effects spatial mixture model to increase model flexibility. The number of clusters is chosen automatically in a data-driven manner. The effectiveness of our approach is demonstrated through a simulation study and motivating application of special tabulations for American Community Survey data.
