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Bootstrap inference for the finite population total under complex sampling designs

時間:2019-10-14

Statistics Seminar (2019-21)

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

Speaker: 王中雷, 廈門大學王亞南經濟研究院

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:

王中雷助理教授畢業于愛荷華州立大學統計系,現就職于廈門大學王亞南經濟研究院。其研究興趣包括抽樣調查以及重抽樣方法。

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


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