Resampling Methods for Survey Data with Extensions in other Contexts

具有其他上下文扩展的调查数据重采样方法

• 批准号: RGPIN-2017-06037
• 负责人: Mashreghi, Zeinab
• 金额: $ 1.02万
• 依托单位: University of Winnipeg
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635838
• 关键词: Resampling; Methods; Survey; Data; Extensions

How accurate is a given statistic? This might be the first question that a researcher asks once a statistic is used to estimate a parameter of interest. Obtaining accuracy measures of a given statistic, such as the variance, is not always easy through analytical methods. That is why resampling methods, such as the bootstrap, have been widely used in the literature to estimate such measurements. In my research program, I intend to study the theoretical developments and practical applications of bootstrap methods in order to establish new ideas.

给定的统计数据有多准确？一旦使用统计数据来估计感兴趣的参数，这可能是研究人员提出的第一个问题。通过分析方法获得给定统计数据的准确度测量（例如方差）并不总是那么容易。这就是为什么重采样方法（例如引导程序）已在文献中广泛使用来估计此类测量值。在我的研究计划中，我打算研究引导方法的理论发展和实际应用，以建立新的想法。