A unified approach to linearization variance estimation from survey data after imputation for item nonresponse
成果类型：
Article
署名作者：
Kim, Jae Kwang; Rao, J. N. K.
署名单位：
Iowa State University; Carleton University
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/asp041
发表日期：
2009
页码：
917932
关键词：
摘要：
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.
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