Topics in statistical analysis with incomplete data

不完整数据的统计分析主题

• 批准号: 327037-2009
• 负责人: Zhao, Yang
• 金额: $ 1.09万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2009
• 资助国家: 加拿大
• 起止时间: 2009-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=428625
• 关键词: Topics; statistical; analysis; incomplete; data

Regression models are commonly used to study the association between a response variable and a set of covariates. Standard inference methods can be directly applied when there is no selection bias (e.g. in simple random sampling design) and the objects selected in the sample are fully observed. However in practice many studies involve some combination of selection bias, missing or mismeasured data. The objectives of the proposed research program are: (i) development and application of estimation methods which can deal with missing or mismeaured data with selection bias; and (ii) investigation of optimal sampling strategies for two-phase or multi-phase studies which maximize the information in observed data.

回归模型通常用于研究响应变量与一组协变量之间的关联。 当不存在选择偏差（例如在简单随机抽样设计中）并且样本中选择的对象被充分观察时，可以直接应用标准推断方法。 然而，在实践中，许多研究涉及选择偏差，缺失或错误测量的数据的组合。 拟议的研究计划的目标是：（一）估计方法，可以处理缺失或mismeared数据与选择偏差的发展和应用;和（二）调查的最佳抽样策略，两个阶段或多个阶段的研究，最大限度地提高观测数据中的信息。