Fractional hot deck imputation for robust inference under item nonresponse in survey sampling

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Parametric fractional imputation (PFI), proposed by Kim (2011), is a tool for general purpose parameter estimation under missing data. We propose a fractional hot deck imputation (FHDI) which is more robust than PFI or multiple imputation. In the proposed method, the imputed values are chosen from the set of respondents and assigned proper fractional weights. The weights are then adjusted to meet certain calibration conditions, which makes the resulting FHDI estimator efficient. Two simulation studies are presented to compare the proposed method with existing methods
Publisher
STATISTICS CANADA
Issue Date
2014-12
Language
English
Article Type
Article
Keywords

NEAREST-NEIGHBOR IMPUTATION; MISSING DATA; VARIANCE-ESTIMATION; MULTIPLE-IMPUTATION; MODELS

Citation

SURVEY METHODOLOGY, v.40, no.2, pp.211 - 230

ISSN
0714-0045
URI
http://hdl.handle.net/10203/212991
Appears in Collection
MA-Journal Papers(저널논문)
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