标题： 信息抽样下的伪后验分布的收敛性 显示英文标题

标题： Convergence of Pseudo Posterior Distributions under Informative Sampling

摘要： 一个信息性抽样设计分配了与感兴趣响应相关的包含概率，并在样本观测之间产生了依赖性。 在信息性抽样设计下获取的数据上进行的未经调整的基于模型的推断，如果在模型中未考虑抽样设计，则可能对生成分布的总体参数产生偏差。 已知的边缘包含概率可以用于加权每个观测单元的似然贡献，以形成一个“伪”后验分布，目的是对设计进行调整。 本文将关于在分析人员指定的模型空间上定义的后验分布在真实生成分布下的一致性理论结果扩展到用于对信息性抽样设计进行调整的抽样加权伪后验分布。 我们构建了关于已知边缘和成对包含概率的条件，这些条件定义了一个抽样设计类，在该类中伪后验的一致性可以在概率上实现。 我们在涉及劳动统计局职位空缺和劳动力流动调查的应用中展示了这一结果。 显示英文摘要

摘要： An informative sampling design assigns probabilities of inclusion that are correlated with the response of interest and induces a dependence among sampled observations. Unadjusted model-based inference performed on data acquired under an informative sampling design can be biased concerning parameters of the population generating distribution if the sample design is not accounted for in the model. Known marginal inclusion probabilities may be used to weight the likelihood contribution of each observed unit to form a "pseudo" posterior distribution with the intent to adjust for the design. This article extends a theoretical result on the consistency of the posterior distribution, defined on an analyst-specified model space, at the true generating distribution to the sampling-weighted pseudo posterior distribution used to account for an informative sampling design. We construct conditions on known marginal and pairwise inclusion probabilities that define a class of sampling designs where consistency of the pseudo posterior is achieved, in probability. We demonstrate the result on an application concerning the Bureau of Labor Statistics Job Openings and Labor Turnover Survey.