论文标题
层次贝叶斯模型的关节点和方差估计用于调查数量数据
Joint Point and Variance Estimation under a Hierarchical Bayesian model for Survey Count Data
论文作者
论文摘要
我们提出了一个新型的贝叶斯框架，用于对计数数据的调查点和方差估计的联合建模。该方法在建模的真实方差上包含了诱导的先验分布，该分布设置为相当于点估计的生成方差，这是一个关键属性，对于连续数据响应类型模型而言，它更容易实现。我们的计数数据模型公式允许在多种分辨率（例如，州，地区，国家）中输入域的输入，并同时对较高分辨率（例如州）对较低分辨率（例如地区）（例如地区）进行了基准测试，以借贷我们的域以更高的分辨率估算的方式，以更高的强度估算。我们进行了一项仿真研究，该研究生成域内的单位人群，以产生地面真理统计数据，以比较对从人群中采集的样品进行的直接和建模估计值，在该样品中我们显示出跨域误差的减少。该模型适用于美国劳工统计局管理的工作空缺和劳动力流动调查中发表的工作空缺和其他数据项。
We propose a novel Bayesian framework for the joint modeling of survey point and variance estimates for count data. The approach incorporates an induced prior distribution on the modeled true variance that sets it equal to the generating variance of the point estimate, a key property more readily achieved for continuous data response type models. Our count data model formulation allows the input of domains at multiple resolutions (e.g., states, regions, nation) and simultaneously benchmarks modeled estimates at higher resolutions (e.g., states) to those at lower resolutions (e.g., regions) in a fashion that borrows more strength to sharpen our domain estimates at higher resolutions. We conduct a simulation study that generates a population of units within domains to produce ground truth statistics to compare to direct and modeled estimates performed on samples taken from the population where we show improved reductions in error across domains. The model is applied to the job openings variable and other data items published in the Job Openings and Labor Turnover Survey administered by the U.S. Bureau of Labor Statistics.
