时间：2019年6月20日(周四)下午15:00-16:00
地点：闵行校区法商南楼135室
题目：Bootstrap inference for the finite population total under complex sampling designs
报告人：王中雷 助理教授 厦门大学
摘要：Bootstrap is a useful tool for making statistical inference, but it may provide erroneous results under complex survey sampling. Most studies about bootstrap-based inference are developed under simple random sampling and stratified random sampling. In this paper, we propose a unified bootstrap method applicable to some complex sampling designs, including Poisson sampling and probability-proportional-to-size sampling. Two main features of the proposed bootstrap method are that studentization is used to make inference, and the finite population is bootstrapped based on a multinomial distribution by incorporating the sampling information. We show that the proposed bootstrap method is second-order accurate using the Edgeworth expansion. Two simulation studies are conducted to compare the proposed bootstrap method with the Wald-type method, which is widely used in survey sampling. Results show that the proposed bootstrap method is better in terms of coverage rate especially when sample size is limited.
报告人简介：王中雷，毕业于爱荷华州立大学统计系，现任厦门大学王亚南经济研究院助理教授。其研究兴趣包括抽样调查以及bootstrap。