Campus Units

Statistics

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

2020

Journal or Book Title

Journal of the American Statistical Association

DOI

10.1080/01621459.2020.1796358

Abstract

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 of Kim (2011) may be subject to bias under model misspecification. In this paper, we propose a novel semiparametric fractional imputation 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 √n- consistency of the semiparametric fractional imputation estimator are also established. Some simulation studies are presented to check the finite sample performance of the proposed method.

Comments

This is a manuscript of an article published as Sang, Hejian, Jae Kwang Kim, and Danhyang Lee. "Semiparametric fractional imputation using Gaussian mixture models for handling multivariate missing data." Journal of the American Statistical Association (2020). doi: 10.1080/01621459.2020.1796358. Posted with permission.

Copyright Owner

American Statistical Association

Language

en

File Format

application/pdf

Published Version

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