Dealing with nonresponse in complex surveys

在复杂和大规模的调查中，在收集的数据集中往往不可避免地存在缺失值。无响应通常会导致估计值的偏差和方差的增加。因此，对无反应的处理对于产生可靠的分析是必要的。然后，需要调整估计值及其精确度，以考虑到治疗。这项研究计划涉及开发方法和工具来处理复杂调查和大数据集中的无反应。在研究计划的第一部分，将研究估计参数的方差的估计方法。我们将建立方差估计量的理论性质。此外，还将对最近开发的估计方法进行综述和比较。有必要揭示方差估计的最新发展，以便利从业人员的工作。在研究计划的第二部分，将开发新的补偿方法来处理多变量无响应，当数据集的几个变量受到缺失值的影响时。将开发一种捐助者归类方法，并研究现代统计学习技术在归类方法中的使用。在调查抽样中，针对多变量无反应的处理方法很少。随着收集大规模数据变得更容易，多变量无反应治疗的进展对于产生可靠的研究是重要的。在研究计划的最后部分，将使用无反应概念来处理不具代表性的样本。不同数据来源的可及性给调查抽样带来了新的挑战。将对不同信息来源造成的覆盖误差进行调查。提出了减少覆盖偏差的方法。这些方法将有可能在重要的应用中使用，例如在森林清查中。

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.