Statistics Seminar (2019-21)

Title: Bootstrap inference for the finite population total under complex sampling designs

Speaker: 王中雷,厦门大学王亚南经济研究院

Time: Thursday, October 24, 14:00-15:00

Place: Room 217, Guanghua Building 2

Abstract:

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.

Introduction:

王中雷助理教授结业于爱荷华州立大学统计系，现就职于厦门大学王亚南经济研究院
。其研究兴趣包括抽样视察以及重抽样要领
。

http://wise.xmu.edu.cn/people/faculty/329d06db-4b6f-4e54-bdd2-4e22479bcee4.html

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