Innovations in Statistical Methodology for Complex Surveys

这一研究项目将使调查抽样工具现代化，以便从调查数据中进行推断，可用于复杂的问题，如调查综合或小面积估计。越来越多的数据源具有传统采样工具难以处理的特性。该项目将解决基于复杂样本数据的重要推理问题，如模型选择和假设检验，并将方法从参数模型扩展到更灵活的非参数模型，如分位数回归。新方法将广泛适用于不同学科的调查，包括农业、卫生和人口统计学。这项研究的产品将包括为从事调查的统计员配备工具，以更好地满足决策者和公众的需求的资源。该项目制作的软件和元数据将向公众开放。研究生将接受教育和培训，重点是基于设计的推理和基于模型的推理之间的关系。这项研究项目将开发创新的方法，使调查统计学家能够以统计上合理的方式利用现代数据结构和模型。该项目将集中在三个领域：(1)使用逆抽样和重新加权从非概率样本中获得有效的推断；(2)复杂样本设计下的预测和分析推理方法；以及(3)适用于大型、多样化数据源的分层建模策略。从非概率样本中获得近似无偏推论的方法具有很高的相关性，因为无反应和使用非调查数据的情况有所增加，例如行政来源和卫星信息。这种复杂样本设计下的推理检验将进一步研究假设检验和半参数模型的使用，特别是在样本设计对指定模型具有信息性的情况下。调查人员将进一步开发一种层次化建模方法，将为大型数据源细分获得的估计汇总在一起。调查人员使用非调查数据对这一程序进行了审查，并将在大规模调查中应用这一方法。

This research project will modernize survey sampling tools for inference from survey data that can be used for complex problems such as survey integration or small area estimation. Increasingly, data sources have characteristics that traditional sampling tools are not readily designed to handle. The project will address important inference problems, such as model selection and hypothesis testing, based on data from complex samples and will extend the methods from parametric models to more flexible nonparametric models, such as quantile regression. The new methodology will have broad applicability to surveys in diverse disciplines, including agriculture, health, and demographics. Products of this research will include resources to equip practicing survey statisticians with tools to better meet the demands of policy makers and the public. Software and metadata produced by this project will be made publicly available. Graduate students will receive education and training, with an emphasis on the relationships between design-based and model-based inference.This research project will develop innovative methods that will enable survey statisticians to exploit modern data structures and models in a statistically defensible way. The project will focus on three areas: (1) the use of inverse sampling and reweighting to obtain valid inferences from nonprobability samples, (2) methods for prediction and analytic inference under complex sample designs, and (3) hierarchical modeling strategies that are feasible to implement with large, diverse data sources. Methods to obtain approximately unbiased inferences from non-probability samples are highly relevant because of increases in nonresponse and use of non-survey data, such as administrative sources and satellite information. This examination of inference under complex sample designs will further research on hypothesis testing and the use of semiparametric models, particularly for situations in which the sample design is informative for the specified model. The investigators will further develop a hierarchical modeling approach that aggregates estimates obtained for sub-divisions of a large data source. The investigators have vetted this procedure using non-survey data and will apply the approach in a large-scale survey context.