Efficient imputation and resampling methods for analyzing complex survey data

用于分析复杂调查数据的高效插补和重采样方法

• 批准号: 227179-2010
• 负责人: Wu, Changbao
• 金额: $ 1.46万
• 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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=546717
• 关键词: Efficient; imputation; resampling; methods; analyzing

Large scale complex surveys are routinely conducted by organizations such as Statistics Canada to gather critical information about the target population. Statistical analysis of survey data provides grounds for informed decisions on management strategies, development plans and policies that affect every aspects of our society. Survey data are also widely used by researchers to address issues in business, education, health, behaviour and other psychosocial and economical areas. Among the challenging issues related to analysis of complex survey data, missing data problems and variance estimation techniques have been two important areas for methodological research. This proposal extends methods that are used in current practice and develops new and efficient imputation methods for handling missing data and efficient resampling procedures for variance estimation. Our proposed methods build on the strength of currently used procedures, provide solutions to some of the problems and will likely change the way that public-use survey data sets are produced and how these data sets are analyzed. The proposed research topics also provide ample opportunities for graduate students to get involved and to develop ideas into their thesis research.

加拿大统计局等组织经常进行大规模的复杂调查，以收集有关目标人口的重要信息。对调查数据的统计分析为就影响我们社会各个方面的管理战略、发展计划和政策作出知情决定提供了依据。调查数据还被研究人员广泛用于解决商业、教育、卫生、行为和其他心理社会和经济领域的问题。在复杂调查数据分析的挑战性问题中，缺失数据问题和方差估计技术一直是方法学研究的两个重要领域。该建议扩展了目前实践中使用的方法，并开发了新的和有效的插补方法来处理缺失数据和有效的方差估计的恢复程序。我们提出的方法建立在目前使用的程序的力量，提供解决方案的一些问题，并可能会改变的方式，公共使用的调查数据集的生产和如何分析这些数据集。拟议的研究课题也为研究生提供了充分的机会，让他们参与并将想法发展到论文研究中。