GENERAL AND FEASIBLE TESTS WITH MULTIPLY-IMPUTED DATASETS

• 成果类型: Article
• 署名作者: Chan, Kin Wai
• 署名单位: Chinese University of Hong Kong
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/21-AOS2132
• 发表日期: 2022
• 页码: 930-948
• 关键词: small-sample degrees; statistical-inference; imputation; likelihood; FRAMEWORK; freedom

摘要

Multiple imputation (MI) is a technique especially designed for handling missing data in public-use datasets. It allows analysts to perform incompletedata 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 incompletedata test.