论文标题
在内容丰富的采样下，用于非高斯数据的计算高效贝叶斯单元级模型
Computationally Efficient Bayesian Unit-Level Models for Non-Gaussian Data Under Informative Sampling
论文作者
论文摘要
传统上，通过基于设计的估计器获得了调查样本的统计估计。在许多情况下，这些估计量倾向于在人口总数或手段等数量中运作良好，但随着样本量变小，可能会降低。在当今的“信息时代”中，人们对更多颗粒状估计的需求很大。为了满足这一需求，使用贝叶斯伪样的样子，我们为在内容丰富的采样设计下收集的非高斯数据提出了一种计算有效的单位级建模方法。具体而言，我们专注于二进制和多项式数据。我们的方法既是多元式的，又是多尺度，在区域层面上融合了空间依赖性。我们通过一项经验模拟研究和使用美国社区调查的健康保险估算的激励应用来说明我们的方法。
Statistical estimates from survey samples have traditionally been obtained via design-based estimators. In many cases, these estimators tend to work well for quantities such as population totals or means, but can fall short as sample sizes become small. In today's "information age," there is a strong demand for more granular estimates. To meet this demand, using a Bayesian pseudo-likelihood, we propose a computationally efficient unit-level modeling approach for non-Gaussian data collected under informative sampling designs. Specifically, we focus on binary and multinomial data. Our approach is both multivariate and multiscale, incorporating spatial dependence at the area-level. We illustrate our approach through an empirical simulation study and through a motivating application to health insurance estimates using the American Community Survey.
