报告人： Prof. Donald B. Rubin (Harvard University)
时间：2018-03-16 14:00 ~ 15:30
地点：Lecture Hall, Jiayibing Building, Jingchunyuan 82, BICMR
There are two relatively standard approaches for dealing with missing data in statistics, one based on “selection models” and one based on “pattern-mixture” models. The former is focused on formulating a model for complete data and then effectively imputing missing data so that the combined observed and missing data fit the assumed model for the complete data. In contrast, the latter effectively fits a different model for each pattern of observed and missing data, thereby directly revealing sensitivity of conclusions to assumptions about distributions for which there are no actual observed data available for estimation. A third class of models, which have remained mostly recondite, is based on “Gibbs” factorizations; although these may not imply a valid joint distribution, they have enjoyed success in applications because of their ease of use when implemented by MCMC computer software for multiple imputation, such as in SAS, STATA, and MICE. The consideration of sensitivity of conclusions to assumptions unassailable by observed data, whether implicit, as with selection models, or explicit, as with pattern-mixture models, is a critical ingredient of satisfactory analyses of data sets with missing values. Graphical displays, such as “enhanced tipping point analyses” implemented using modern computing, are critical ingredients for this enterprise.
About the Speaker:
Donald B. Rubin，哈佛大学John L. Loeb教授,美国科学院院士, 美国艺术与科学院院士, 美国科学促进会会士。Rubin教授是当今世界影响力最深远的统计学家之一，他在现代统计领域做出了许多基础贡献，特别是在缺失数据和因果推断方面。他发表了400余篇论文，这些论文被多次引用，仅在2016年一年内的引用次数就超过了20000次。