Semiparametric Fractional Imputation Using Gaussian Mixture Models for Handling Multivariate Missing Data

• 成果类型: Article
• 署名作者: Sang, Hejian; Kim, Jae Kwang; Lee, Danhyang
• 署名单位: Alphabet Inc.; Google Incorporated; Iowa State University; University of Alabama System; University of Alabama Tuscaloosa
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1796358
• 发表日期: 2022
• 页码: 654-663
• 关键词: multiple-imputation; likelihood; distributions; selection; VALUES

摘要

Item nonresponse is frequently encountered in practice. Ignoring missing data can lose efficiency and lead to misleading inference. Fractional imputation is a frequentist approach of imputation for handling missing data. However, the parametric fractional imputation may be subject to bias under model misspecification. In this article, we propose a novel semiparametric fractional imputation (SFI) method using Gaussian mixture models. The proposed method is computationally efficient and leads to robust estimation. The proposed method is further extended to incorporate the categorical auxiliary information. The asymptotic model consistency and root n-consistency of the SFI estimator are also established. Some simulation studies are presented to check the finite sample performance of the proposed method.