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Article
Semiparametric fractional imputation using Gaussian mixture models for handling multivariate missing data
arxiv
  • Hejian Sang, Google Inc
  • Jae Kwang Kim, Iowa State University
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
9-18-2018
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 pre-print is made available through arxiv: https://arxiv.org/abs/1809.05976.

Creative Commons License
Creative Commons Attribution-Share Alike 4.0
Copyright Owner
The Authors
Language
en
File Format
application/pdf
Citation Information
Hejian Sang and Jae Kwang Kim. "Semiparametric fractional imputation using Gaussian mixture models for handling multivariate missing data" arxiv (2018)
Available at: http://works.bepress.com/jae-kwang-kim/46/