Abstract
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 nonstandard 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.
Funding Statement
The author acknowledges the financial support from the Early Career Scheme (24306919) provided by the University Grant Committee of Hong Kong.
Acknowledgments
A part of the theoretical results in this article are partially developed from the author’s Ph.D. thesis under the supervision of Xiao-Li Meng, who provided many insightful ideas that greatly contribute to this paper. The author would also like to thank the anonymous referees, an Associate Editor and the Editor for their constructive comments that improved the scope and presentation of the paper.
Citation
Kin Wai Chan. "General and feasible tests with multiply-imputed datasets." Ann. Statist. 50 (2) 930 - 948, April 2022. https://doi.org/10.1214/21-AOS2132
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