Parametric fractional imputation for missing data analysis

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Parametric fractional imputation is proposed as a general tool for missing data analysis. Using fractional weights, the observed likelihood can be approximated by the weighted mean of the imputed data likelihood. Computational efficiency can be achieved using the idea of importance sampling and calibration weighting. The proposed imputation method provides efficient parameter estimates for the model parameters specified in the imputation model and also provides reasonable estimates for parameters that are not part of the imputation model. Variance estimation is discussed and results from a limited simulation study are presented
Publisher
OXFORD UNIV PRESS
Issue Date
2011-03
Language
English
Article Type
Article
Keywords

EM ALGORITHM; INCOMPLETE DATA; INFERENCE; LIKELIHOOD; MODELS

Citation

BIOMETRIKA, v.98, no.1, pp.119 - 132

ISSN
0006-3444
DOI
10.1093/biomet/asq073
URI
http://hdl.handle.net/10203/212940
Appears in Collection
MA-Journal Papers(저널논문)
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