A unified approach to linearization variance estimation from survey data after imputation for item nonresponse

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Variance estimation after imputation is an important practical problem in survey sampling. When deterministic imputation or stochastic imputation is used, we show that the variance of the imputed estimator can be consistently estimated by a unifying linearize and reverse approach. We provide some applications of the approach to regression imputation, fractional categorical imputation, multiple imputation and composite imputation. Results from a simulation study, under a factorial structure for the sampling, response and imputation mechanisms, show that the proposed linearization variance estimator performs well in terms of relative bias, assuming a missing at random response mechanism
Publisher
OXFORD UNIV PRESS
Issue Date
2009-12
Language
English
Article Type
Article
Keywords

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Citation

BIOMETRIKA, v.96, no.4, pp.917 - 932

ISSN
0006-3444
DOI
10.1093/biomet/asp041
URI
http://hdl.handle.net/10203/212946
Appears in Collection
MA-Journal Papers(저널논문)
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