Efficiency, Sparsity and Validity in Analyzing Complex Survey Data

分析复杂调查数据的效率、稀疏性和有效性

• 批准号: RGPIN-2015-05613
• 负责人: Wu, Changbao
• 金额: $ 2.26万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=668951
• 关键词: Efficiency; Sparsity; Validity; Analyzing; Complex

Large scale complex surveys play an important role in providing information for policy makers and the general public as well as many scientific areas, such as public health and social science research. The proposed research addresses three critical aspects of complex survey data analysis, namely, efficiency, sparsity and validity, through four broad research projects: (i) Efficient semiparametric fractional imputation for nonresponses and missing data; (ii) Sparse and efficient replication weights and resampling methods for variance estimation; (iii) Bayesian empirical likelihood methods for valid and efficient design-based inferences; and (iv) Efficient sampling techniques and valid inference procedures for big data problems. ***The first two projects strive to provide more attractive strategies and solutions to the two seemingly separate but entangled problems on missing data problems and variance estimation techniques, which are fundamentally important to complex survey data analysis. The third project attempts to develop a general framework and useful methodologies in Bayesian analysis for survey data with complex sampling design features involving stratification, clustering and unequal probability selection. The primary goal is to develop Bayesian analysis procedures with valid frequentist interpretation under the design-based framework. The last project tries to catch the current trend on big data problems, and our potential contribution is to adequately address issues in analyzing overly large data sets with sampling techniques originally developed for finite population problems. ***The anticipated outcomes of this research will be efficient, sparse and valid inference tools and strategies for creating public use micro survey data files and for conducting statistical analysis of complex surveys. 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.**

大规模复杂调查在为政策制定者和公众以及公共卫生和社会科学研究等许多科学领域提供信息方面发挥着重要作用。拟议的研究通过四个广泛的研究项目解决了复杂调查数据分析的三个关键方面，即效率、稀疏性和有效性： (i) 对无答复和缺失数据进行有效的半参数分数插补； (ii) 稀疏且高效的复制权重和方差估计的重采样方法； (iii) 用于有效且高效的基于设计的推论的贝叶斯经验似然法； (iv) 针对大数据问题的高效采样技术和有效推理程序。 ***前两个项目致力于为丢失数据问题和方差估计技术这两个看似独立但又相互纠缠的问题提供更具吸引力的策略和解决方案，这对于复杂的调查数据分析至关重要。第三个项目试图为具有复杂抽样设计特征（涉及分层、聚类和不等概率选择）的调查数据开发贝叶斯分析的通用框架和有用的方法。主要目标是在基于设计的框架下开发具有有效频率解释的贝叶斯分析程序。最后一个项目试图抓住大数据问题的当前趋势，我们的潜在贡献是利用最初为有限总体问题开发的采样技术来充分解决分析过大数据集的问题。 ***这项研究的预期成果将是高效、稀疏和有效的推理工具和策略，用于创建公众使用的微观调查数据文件和对复杂调查进行统计分析。提案中概述的所有四个主要项目都将涉及对硕士和​​博士级别的研究生以及博士后研究员的培训。 **