Unit‐level models for survey data offer many advantages over their area‐level counterparts, such as potential for more precise estimates and a natural benchmarking property. However, two main challenges occur in this context: accounting for an informative survey design and handling non‐Gaussian data types. The pseudo‐likelihood approach is one solution to the former, and conjugate multivariate distribution theory offers a solution to the latter. By combining these approaches, we attain a unit‐level model for count data that accounts for informative sampling designs and includes a fully Bayesian model uncertainty propagation. Importantly, conjugate full conditional distributions hold under the pseudo‐likelihood, yielding an extremely computationally efficient approach. Our method is illustrated via an empirical simulation study using count data from the American Community Survey public use microdata sample.

中文翻译：

---

信息抽样设计下的计数数据的共轭贝叶斯单元级建模

单位面积的调查数据模型比区域级模型具有许多优势，例如潜在的更精确的估算和自然的基准属性。但是，在这种情况下会遇到两个主要挑战：考虑信息量大的调查设计和处理非高斯数据类型。伪似然法是前者的一种解决方案，共轭多元分布理论为后者提供了解决方案。通过组合这些方法，我们获得了用于计数数据的单位级模型，该模型说明了信息性抽样设计，并且包括完整的贝叶斯模型不确定性传播。重要的是，共轭全条件分布在伪可能性下成立，从而产生了一种计算效率极高的方法。