Bootstrap inference for the finite population total under complex sampling designs Wang, Zhonglei Kim, Jae Kwang Kim, Jae Kwang Peng, Liuhua
dc.contributor.department Statistics 2019-09-19T03:44:37.000 2020-07-02T06:57:29Z 2020-07-02T06:57:29Z Tue Jan 01 00:00:00 UTC 2019 2019-01-07
dc.description.abstract <p>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.</p>
dc.description.comments <p>This pre-print is made available through arxiv: <a href=""></a>.</p>
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dc.identifier archive/
dc.identifier.articleid 1259
dc.identifier.contextkey 15137880
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/255
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 22:58:42 UTC 2022
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Statistical Theory
dc.title Bootstrap inference for the finite population total under complex sampling designs
dc.type article
dc.type.genre article
dspace.entity.type Publication
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relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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