A unified approach to linearization variance estimation from survey data after imputation for item nonresponse
Rao, J. N. K.
Kim, Jae Kwang
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.
This is a pre-copyedited, author-produced PDF of an article accepted for publication in Biometrika following peer review. The version of record (Kim, Jae Kwang, and J. N. K. Rao. "A unified approach to linearization variance estimation from survey data after imputation for item nonresponse." Biometrika 96, no. 4 (2009): 917-932) is online at doi:10.1093/biomet/asp041. Posted with permission.