Robust inference for complex survey data

复杂调查在为决策者和一般公众以及公共卫生和社会科学研究等其他领域的许多科学研究提供信息方面发挥着重要作用。自20世纪50年代以来，国家统计局（NSO）一直在使用概率抽样方法，并根据所谓的基于设计的框架进行推论，这可以被视为一个无模型的框架。近十年来，模型在调查抽样中的作用越来越重要。这种范式的转变可以用三个主要因素来解释：(i)回复率下降；（ii）高昂的数据收集成本和（iii）包括网络调查面板和卫星信息在内的非概率数据源的激增。该项目的主要目标是通过四个广泛的项目提出解决这些挑战的新策略：(a)在样本匹配和倾向得分估计的背景下增加稳健的估计程序。(b)对包括分布函数和分位数在内的复杂参数进行多重鲁棒输入程序。(c)小面积估计的抗离群值方法和有限种群均值和平均处理效果的多重稳健估计；(d)制定利用单位的条件偏见追踪非答复者和选择性编辑的策略。多重稳健程序使用多个结果回归模型和/或多个倾向评分模型。如果一个过程在除一个模型外的所有模型都被错误指定时保持一致，则该过程被称为多重鲁棒性。前两个项目力求为缺失数据问题和统计匹配这两个看似独立但又纠缠在一起的问题提供有吸引力的策略和解决方案。第三个项目试图开发估算程序，使其在存在有影响力的单位时均方误差较低，这在商业调查中很常见。最后的项目将提供方法来决定在单位不响应的情况下追逐哪个应答者，以及在选择编辑的情况下哪个单元应该进行编辑。提案中概述的所有四个广泛项目都将涉及培养硕士和博士研究生以及博士后。

Complex surveys play an important role in providing information for policy makers and the general public as well as many scientific in other areas, such as public health and social science research. Since the 1950's, National Statistical Offices (NSO) have been using probability sampling methods and inferences were conducted with respect to the so--called design--based framework, which can be viewed as a model--free framework. In the last decade, the role of models in survey sampling has become more and more important. This shift of paradigm can be explained by three main factors: (i) decreasing response rates; (ii) high data collection costs and (iii) the proliferation of non--probability data sources that include web survey panels and satellite information. The primary objective of this project is to propose new strategies for addressing these challenges through four broad projects: (a) Multiply robust estimation procedures in the context of sample matching and propensity score estimation. (b) Multiply robust imputation procedures for complex parameters including distribution function and quantiles. (c) Outlier--resistant methods for small area estimation and multiply robust estimators of finite population means and average treatment effects; (d) Development of strategies for chasing nonrespondents and selective editing using the conditional bias of a unit. Multiply robust procedures make use of multiple outcome regression models and/or multiple propensity score models. A procedure is said to be multiply robust if it remains consistent when all but one of the models are misspecified. The first two projects strive to provide attractive strategies and solutions to the two seemingly separate but entangled problems on missing data problems and statistical matching. The third project attempts to develop estimation procedures that exhibit low mean square errors in the presence of influential units, which are common in business surveys. The final project will provide methods for deciding which respondents to chase in the context of unit nonresponse and which unit should undergo editing in a context of selecting editing. All four broad projects outlined in the proposal will involve training of graduate students at both master's and PhD levels and of postdoctoral fellows.