Computationally efficient Bayesian unit-level models for non-Gaussian data under informative sampling with application to estimation of health insurance coverage
Computationally efficient Bayesian unit-level models for non-Gaussian data under informative sampling with application to estimation of health insurance coverage
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DOI：
10.1214/21-aoas1524
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发表时间：
2020-09
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影响因子：
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通讯作者：
Paul A. Parker;S. Holan;R. Janicki
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文献类型：
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作者：
Paul A. Parker;S. Holan;R. Janicki
调查样本的统计估计传统上是通过基于设计的估计器获得的。在许多情况下，这些估计量往往适用于人口总数或均值等数量，但随着样本量变小，可能会出现不足。在当今的“信息时代”，强烈需要更精细的估计。为了满足这一需求，我们使用贝叶斯伪似然，提出了一种计算有效的单元级建模方法，用于在信息抽样设计下收集的非高斯数据。具体来说，我们关注二进制和多项数据。我们的方法是多变量和多尺度的，结合了区域层面的空间依赖性。我们通过实证模拟研究以及通过美国社区调查对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.