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

• Published in: Journal of the American Statistical Association Vol. 117; no. 538; pp. 654 - 663
• Main Authors: Sang, Hejian, Kim, Jae Kwang, Lee, Danhyang
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis 03.04.2022; Taylor & Francis Ltd
• Subjects: Americans; Asymptotic methods; Consistency; Missing data; mixtures; Multivariate analysis; Probabilistic models; Regression analysis; sampling; Simulation; Statistical methods; Statistics
• ISSN: 0162-1459; 1537-274X; 1537-274X
• DOI: 10.1080/01621459.2020.1796358

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 -consistency of the SFI estimator are also established. Some simulation studies are presented to check the finite sample performance of the proposed method. Supplementary materials for this article are available online.