Speaker: Zhonglei Wang / Abstract: Bootstrap is a useful tool for making statistical inference, but it may provide erroneous results in survey sampling if the sampling design is ignored. Most studies about bootstrap-based inference are developed under simple random sampling and stratified random sampling. In this work, we propose a new bootstrap method applicable to some complex sampling designs, including Poisson sampling and probability proportional to size sampling. The main feature of the proposed method is that the finite population is bootstrapped based on a multinomial distribution by incorporating the sampling weights. We show that the proposed method is second-order accurate using the Edgeworth expansion. Two simulation studies are conducted to compare the proposed method with the Wald-type method, and results show that the proposed method is better in terms of coverage rate.