Parametric fractional imputation for nonignorable missing data

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Parameter estimation with missing data is a frequently encountered problem in statistics. Imputation is often used to facilitate the parameter estimation by simply applying the complete-sample estimators to the imputed dataset. In this article, we consider the problem of parameter estimation with nonignorable missing data using the approach of parametric fractional imputation proposed by Kim (2011). Using the fractional weights, the E-step of the EM algorithm can be approximated by the weighted mean of the imputed data likelihood where the fractional weights are computed from the current value of the parameter estimates. Calibration fractional imputation is also considered as a way for improving the Monte Carlo approximation in the fractional imputation. Variance estimation is also discussed. Results from two simulation studies are presented to compare the proposed method with the existing methods. A real data example from the Korea Labor and Income Panel Survey (KLIPS) is also presented. (c) 2011 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved
Publisher
KOREAN STATISTICAL SOC
Issue Date
2012-09
Language
English
Article Type
Article
Keywords

GENERALIZED LINEAR-MODELS; MULTIPLE-IMPUTATION; INCOMPLETE DATA; MIXTURE-MODELS; EM ALGORITHM; NONRESPONSE

Citation

JOURNAL OF THE KOREAN STATISTICAL SOCIETY, v.41, no.3, pp.291 - 303

ISSN
1226-3192
DOI
10.1016/j.jkss.2011.10.022
URI
http://hdl.handle.net/10203/213009
Appears in Collection
MA-Journal Papers(저널논문)
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