时空潜在成分建模的地理参考健康数据。
Space-time latent component modeling of geo-referenced health data.
机构信息
Division of Biostatistics and Epidemiology, College of Medicine, Medical University of South Carolina, Charleston, SC, USA.
出版信息
Stat Med. 2010 Aug 30;29(19):2012-27. doi: 10.1002/sim.3917.
Abstract
Latent structure models have been proposed in many applications. For space-time health data it is often important to be able to find the underlying trends in time, which are supported by subsets of small areas. Latent structure modeling is one such approach to this analysis. This paper presents a mixture-based approach that can be applied to component selection. The analysis of a Georgia ambulatory asthma county-level data set is presented and a simulation-based evaluation is made.
摘要
潜结构模型已在许多应用中提出。对于时空健康数据，通常重要的是能够找到时间上的潜在趋势，这些趋势得到小区域子集的支持。潜结构建模就是这种分析方法之一。本文提出了一种基于混合的方法，可应用于组件选择。介绍了对佐治亚州门诊哮喘县级数据集的分析，并进行了基于模拟的评估。
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