General and Feasible Multiple Imputation Tests
10th December 2021, 3:00 pm – 4:00 pm
Virtual Seminar, Zoom link: TBA
Multiple imputation (MI) is a technique especially designed for handling missing data in public-use datasets. It allows analysts to perform incomplete-data inference straightforwardly by using several already imputed datasets released by the dataset owners. However, the existing MI tests require either a restrictive assumption on the missing-data mechanism, known as equal odds of missing information (EOMI), or an infinite number of imputations. Some of them also require analysts to have access to restrictive or non-standard computer subroutines. Besides, the existing MI testing procedures cover only Wald’s tests and likelihood ratio tests but not Rao’s score tests, therefore, these MI testing procedures are not general enough. In addition, the MI Wald’s tests and MI likelihood ratio tests are not procedurally identical, so analysts need to resort to distinct algorithms for implementation. In this paper, we propose a general MI procedure, called stacked multiple imputation (SMI), for performing Wald’s tests, likelihood ratio tests and Rao’s score tests by a unified algorithm. SMI requires neither EOMI nor an infinite number of imputations. It is particularly feasible for analysts as they just need to use a complete-data testing device for performing the corresponding incomplete-data test.
Kin Wai Chan is an Assistant Professor in the Department of Statistics. He completed his B.Sc. and M.Phil. in Risk Management Science in 2013 and 2015, respectively. After that, he did graduate work in Statistics from Harvard University under the supervision of Xiao-Li Meng and received his Ph.D. in 2018. His research interest is statistical inference for dependent data and incomplete data. Many of his research articles are about multiple imputation, long-run variance estimation, and change-point problems. He is particularly keen on developing elegant statistical theories and creating new methodologies that strike a nice balance between statistical and computational properties.