Dealing with nonresponse in complex surveys

在复杂和大规模的调查中，在收集的数据集中存在缺失值往往是不可避免的。无响应通常会引入偏倚并增加估计量的方差。因此，对无应答的处理对于产生可靠的分析是必要的。然后，需要调整估计量及其精度估计，以考虑治疗。该研究计划涉及的方法和工具的发展，以处理在复杂的调查和大型数据集的无响应。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。将建立方差估计量的理论性质。此外，最近开发的估计方法将进行综述和比较。有必要阐明方差估计的最新发展，以方便从业人员的工作。在研究计划的第二部分，将开发新的插补方法来处理多变量无应答，当一个数据集的几个变量受到缺失值。将制定一种捐助者估算方法，并将调查现代统计学习技术在估算方法中的使用情况。在调查抽样中，对于多变量无应答的处理方法很少。随着大规模数据的收集变得越来越容易，多变量无应答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，无反应的概念将用于处理非代表性样本。不同数据来源的可及性给调查抽样带来了新的挑战。将对不同信息来源造成的覆盖误差进行调查。将提出减少覆盖偏差的方法。将有可能在森林清查等重要应用中使用这些方法。

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.