数据丢失困扰着许多应用数据分析。许多数据分析员只是丢弃缺少值的案例，但这可能会导致推理问题。在我们的系中，一些教师正在开发新颖的理论和方法来处理丢失的数据。主要推力是扩展多重插补的理论和应用，该技术最初被认为是一种工具，统计机构可以使用该工具来处理向公众发布的大型数据集中的无响应。统计机构的基本思想是通过从缺失值的预测分布中采样来反复模拟缺失数据的值。这将创建多个完整的数据集，这些数据集将公开发布。教师正在开发更好的方法来估算高维数据集中的缺失数据，处理系统性缺失的数据（在随机数据中所谓的“不缺失”），并
Missing data plague many applied data analyses. Many data analysts simply drop cases with missing values, but this can cause problems for inferences. In our department, several faculty are developing novel theory and methods for handling missing data. A major thrust is to extend the theory and applications of multiple imputation, a technique conceived initially as a tool that statistical agencies could use to handle nonresponse in large datasets disseminated to the public. The basic idea is for the statistical agency to simulate values for the missing data repeatedly by sampling from predictive distributions of the missing values. This creates multiple, completed datasets that are disseminated to the public. Faculty are developing better ways to impute missing data in high dimensional datasets, to handle data that are systematically missing (so called not missing at random data), and to improve theory for inferences. Faculty also work on principled, model-based methods for dealing with data reported with errors, such as someone who reports being a pregnant male. Application areas include government statistics, health data, and large scale surveys and censuses in political science, economics, psychology, and sociology.