BAYESIAN NONPARAMETRIC MULTIVARIATE SPATIAL MIXTURE MIXED EFFECTS MODELS WITH APPLICATION TO AMERICAN COMMUNITY SURVEY SPECIAL TABULATIONS

• 成果类型: Article
• 署名作者: Janicki, Ryan; Raim, Andrew M.; Holan, Scott H.; Maples, Jerry J.
• 署名单位: University of Missouri System; University of Missouri Columbia
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1494
• 发表日期: 2022
• 页码: 144-168
• 关键词: spatiotemporal models; sampling methods

摘要

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 in which the data exhibit heterogeneity within or across geographies, and/or there is 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.