Bayesian Spatial Change of Support for Count-Valued Survey Data With Application to the American Community Survey
计数型调查数据的贝叶斯空间支撑转换：美国社区调查应用AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要聚焦空间计数调查与区域支撑转换，现有细分中没有直接对应方向。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
计数型调查数据的贝叶斯空间支撑转换：美国社区调查应用
- 研究问题
- 如何将既定地理区域上的计数型调查估计转换到用户自定义区域，并在转换中纳入已知调查方差？
- 方法与思路
- 作者采用泊松分布建模计数，将小区域计数解释为空间点过程事件的聚合，并结合调查误差信息进行贝叶斯分析。
- 材料陈述的贡献
- 摘要称，该方法支持灵活转换空间支撑，并通过模拟及美国社区调查公开数据展示其应用效果。
- 阅读关注点
- 应核对点过程与区域聚合的联系、调查方差的纳入方式，以及新区域估计的不确定性和实时计算代价。
- 阅读边界
- 仅据稿件版本未注明的 OpenAlex 索引摘要；具体模型假设、误差传播和性能数值未说明，不能推断刊本变化。
原文摘要
Jonathan R. Bradley*, Christopher K. Wikle & Scott H. HolanCONTACT Jonathan R. Bradley bradleyjr@missouri.edu Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO 65211-6100Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.ABSTRACTWe introduce Bayesian spatial change of support (COS) methodology for count-valued survey data with known survey variances. Our proposed methodology is motivated by the American Community Survey (ACS), an ongoing survey administered by the U.S. Census Bureau that provides timely information on several key demographic variables. Specifically, the ACS produces 1-year, 3-year, and 5-year “period-estimates,” and corresponding margins of errors, for published demographic and socio-economic variables recorded over predefined geographies within the United States. Despite the availability of these predefined geographies, it is often of interest to data-users to specify customized user-defined spatial supports. In particular, it is useful to estimate demographic variables defined on “new” spatial supports in “real-time.” This problem is known as spatial COS, which is typically performed under the assumption that the data follow a Gaussian distribution. However, count-valued survey data is naturally non-Gaussian and, hence, we consider modeling these data using a Poisson distribution. Additionally, survey-data are often accompanied by estimates of error, which we incorporate into our analysis. We interpret Poisson count-valued data in small areas as an aggregation of events from a spatial point process. This approach provides us with the flexibility necessary to allow ACS users to consider a variety of spatial supports in “real-time.” We show the effectiveness of our approach through a simulated example as well as through an analysis using public-use ACS data.