Robust inference for complex survey data

对复杂调查数据的稳健推断

• 批准号: RGPIN-2019-05891
• 负责人: Haziza, David
• 金额: $ 3.06万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688540
• 关键词: Robust; inference; complex; survey; data

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.*****

复杂的调查在为决策者和公众提供信息方面发挥着重要作用，在其他领域，如公共卫生和社会科学研究中，也发挥着重要的科学作用。自1950年代以来，国家统计局一直在使用概率抽样方法，并对所谓的基于设计的框架进行了推断，该框架可被视为无模型框架。近十年来，模型在调查抽样中的作用越来越重要。这种模式的转变可以用三个主要因素来解释：㈠答复率下降; ㈡数据收集费用高; ㈢包括网络调查小组和卫星信息在内的非概率数据来源激增。该项目的主要目标是提出新的战略，通过四个广泛的项目应对这些挑战：（a）在样本匹配和倾向分数估计方面增加可靠的估计程序。(b)复杂参数（包括分布函数和分位数）的多重稳健插补方法。(c)小面积估计和有限总体平均数和平均治疗效果的多重稳健估计的抗离群值方法;（d）制定追踪未答复者和使用单位条件偏差进行选择性编辑的战略。多重稳健程序利用多个结果回归模型和/或多个倾向评分模型。一个过程被称为多重鲁棒的，如果它保持一致，当所有的模型，但其中一个是错误的。前两个项目致力于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计程序，在有影响力的单位存在的情况下，表现出低均方误差，这在商业调查中很常见。最后的专题将提供方法，以决定在单元不答复的情况下追踪哪些答复者，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硕士和博士两级研究生以及博士后研究员的培训。