Bootstrap Variance Estimation for Rejective Sampling
将Bootstrap方法扩展到拒绝抽样，通过从原始样本中抽取有放回的拒绝样本进行方差估计，并针对小层数分层样本提出改进，模拟验证了回归估计量方差估计的有效性。
Replication procedures have proven useful for variance estimation for large scale complex surveys. As an extension of bootstrap procedures to rejective samples, we define a bootstrap sample that is a rejective, unequal probability, replacement sample selected from the original sample. A modification of the bootstrap with improved performance is suggested for stratified samples with small stratum sizes. Simulations for Poisson and stratified rejective samples support the use of replicates in estimating the variance of the regression estimator for rejective samples.