Standard randomization-based inference conditions on the data in the population and makes inference with respect to the repeating sampling properties of the sampling indicators. In some settings these estimators can be quite unstable; Bayesian model-based approaches focus on the posterior predictive distribution of population quantities, potentially providing a better balance between bias correction and efficiency. Previous work in this area has focused on estimation of means and linear and generalized linear regression parameters; these methods do not allow for a general estimation of distributional functions such as quantile or quantile regression parameters. Here we adapt an extended Dirichlet Process Mixture model that allows the DP prior to be a mixture of DP random basis measures that are a function of covariates. These models allow many mixture components when necessary to accommodate the sample design, but can shrink to few components for more efficient estimation when the data allow. We provide an application to the estimation of relationships between serum dioxin levels and age in the US population, either at the mean level (via linear regression) or across the dioxin distribution (via quantile regression) using the National Health and Nutrition Examination Survey.

中文翻译：

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用于适应线性和分位数回归的复杂样本设计的加权狄利克雷过程混合模型。

对总体中的数据进行标准的基于随机化的推断条件，并根据抽样指标的重复抽样属性进行推断。在某些情况下，这些估计量可能非常不稳定；基于贝叶斯模型的方法侧重于人口数量的后验预测分布，可能在偏差校正和效率之间提供更好的平衡。该领域以前的工作主要集中在均值和线性和广义线性回归参数的估计上；这些方法不允许对分布函数（如分位数或分位数回归参数）进行一般估计。在这里，我们采用了扩展的狄利克雷过程混合模型，该模型允许 DP 之前是作为协变量函数的 DP 随机基础度量的混合。这些模型在必要时允许使用许多混合成分以适应样本设计，但在数据允许的情况下可以缩小到少数成分以进行更有效的估计。我们提供了一个应用程序，用于估计美国人口中血清二恶英水平与年龄之间的关系，无论是在平均水平（通过线性回归）还是整个二恶英分布（通过分位数回归）使用国家健康和营养检查调查。