Dealing with nonresponse in complex surveys

处理复杂调查中的无答复问题

• 批准号: RGPIN-2020-05458
• 负责人: Vallée, AudreyAnne
• 金额: $ 1.31万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716894
• 关键词: Dealing; nonresponse; complex; surveys

In complex and large scale surveys, the presence of missing values in the collected data sets is often inevitable. Nonresponse generally introduces a bias and an increase in the variance of the estimator. Hence, the treatment of nonresponse is necessary to produce reliable analysis. The estimators and the estimation of their precision then need to be adapted to account for the treatment. This research program concerns the development of methods and tools to deal with nonresponse in complex surveys and large data sets. In a first part of the research program, the estimation methods of the variance of estimated parameters will be investigated. Theoretical properties of variance estimators will be established. Also, the estimation methods that have been recently developed will be overviewed and compared. There is a need to unravel the recent developments in variance estimation to facilitate the work of practitioners. In a second part of the research program, new imputation methods will be developed to deal with multivariate nonresponse, when several variables of a data set are subject to missing values. A donor imputation method will be developed and the use of modern statistical learning techniques in imputation methods will be investigated. In survey sampling, few treatments have been developed for multivariate nonresponse. As it becomes easier to collect large scale data, the progresses in the treatment of multivariate nonresponse are important to produce reliable studies. In a last part of the research program, nonresponse notions will be used to deal with non-representative samples. The accessibility to different sources of data brings new challenges in survey sampling. Coverage errors resulting from the different sources of information will be investigated. Methods to reduce the coverage biases will be proposed. It will be possible to use these methods in important applications such as in forest inventories.

在复杂和大规模的调查中，在所收集的数据集中存在缺失值通常是不可避免的。无响应通常会导致估计量的偏差和方差的增加。因此，无反应的处理是必要的，以产生可靠的分析。然后需要调整估计器及其精度的估计以考虑处理。这个研究项目涉及的方法和工具的发展，以处理在复杂的调查和大数据集的无反应。在研究计划的第一部分中，将研究估计参数方差的估计方法。将建立方差估计的理论性质。此外，还将对最近开发的评估方法进行概述和比较。有必要揭示方差估计的最新发展，以促进从业者的工作。在研究计划的第二部分，将开发新的imputation方法来处理多变量无响应，当一个数据集的几个变量受到缺失值。将开发一种供体归算方法，并将研究在归算方法中使用现代统计学习技术。在调查抽样中，很少有针对多变量无反应的治疗方法。随着大规模数据的收集变得更加容易，多变量无反应治疗的进展对于产生可靠的研究非常重要。在研究计划的最后一部分，非响应概念将用于处理非代表性的样本。不同数据来源的可及性给调查抽样带来了新的挑战。将调查由不同信息来源引起的报道误差。将提出减少覆盖偏差的方法。这些方法将有可能用于诸如森林清查等重要应用。