贝叶斯模型用于空间计数数据与有信息的有限人群,适用于美国社区调查
1Department of Statistics, Florida State University, Tallahassee, FL, USA.
Journal of applied statistics
|September 18, 2023
概括
美国社区调查 (ACS) 使用新的联合统计模型来改进对贫困等人口统计数据的空间预测. 与传统模型相比,这种方法更好地模拟了人口计数和有限人口之间的关系.
科学领域:
- 统计 统计 统计 统计
- 人口统计学 人口统计学
- 空间分析 空间分析
背景情况:
- 美国社区调查 (ACS) 为行政区提供了关键的人口统计估计.
- 当前的模型通常假定给定有限种群的计数结果的二项式分布,缺乏联合关系定义.
- 这种限制可能会影响空间预测的准确性.
研究的目的:
- 在ACS框架内,为计数值结果和有限种群开发和评估一个共同的统计模型.
- 通过定义联合关系来解决条件指定 (CS) 模型的局限性.
- 通过利用结果和人口之间的相互依赖来提高空间预测的准确性.
主要方法:
- 提出了一种联合模型,将计数视为二项式,给定有限人口和有限人口作为负二项式.
- 采用了多变量逻辑-β先前分布.
- 一个高效的吉布斯采样器是使用封闭形式的全条件分布开发出来的.
主要成果:
- 模拟和对ACS贫困估计的应用证明了该模型的有效性.
- 拟议的联合模型显示了相对于传统的CS双项模型的优势.
- 当交叉依赖改善空间预测时,有限的群体被证明是"有信息的".
结论:
- 开发的联合模型为ACS数据提供了改进的空间预测.
- 这种方法提供了一个比传统的CS模型更强大的统计框架.
- 这些发现强调了模拟共同分布的重要性,以准确地估计人口统计数据.
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