Some Prediction Properties of Balanced Half-Sample Variance Estimators in Single-Stage Sampling

The balanced half-sample (BHS) method of variance estimation is a widely used technique in complex sample surveys. Large-sample prediction properties of the BHS method are given here for the separate ratio and regression estimators. Results are obtained when a large number of units are sampled and divided into two groups within each stratum. An empirical study examines the conditional and unconditional performance of the BHS method when estimating mean squared errors and constructing confidence intervals. The study also includes comparisons with the jackknife and the linearization estimators.