Resampling Variance Estimation in Surveys with Missing Data
A.C. Davison, S. Sardy
We discuss variance estimation by resampling in surveys in which data are missing. We derive a formula for linearization in the case of calibrated estimation with deterministic regression imputation, and compare the resulting variance estimates with balanced repeated replication with and without grouping, the bootstrap, the block jackknife, and multiple imputation, for simulated data based on the Swiss Household Budget Survey. We also investigate the number of replications needed for reliable variance estimation under resampling in this context. Linearization, the bootstrap, and multiple imputation perform best in terms of relative bias and mean squared error.
Balanced repeated replication, bootstrap, calibration, influence function, jackknife, linearization, missing data, multiple imputation