Population empirical likelihood for nonparametric inference in survey sampling

dc.contributor.author Chen, Sixia
dc.contributor.author Kim, Jae Kwang
dc.contributor.author Kim, Jae Kwang
dc.contributor.department Statistics
dc.date 2018-02-18T16:56:16.000
dc.date.accessioned 2020-07-02T06:56:35Z
dc.date.available 2020-07-02T06:56:35Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2014
dc.date.issued 2014-01-01
dc.description.abstract <p><blockquote> <blockquote>Empirical likelihood is a popular tool for incorporating auxiliary information and constructing nonparametric confidence intervals. In survey sampling, sample elements are often selected by using an unequal probability sampling method and the empirical likelihood function needs to be modified to account for the unequal probability sampling. Wu and Rao (2006) proposed a way of constructing confidence regions using the pseudo empirical likelihood of Chen and Sitter (1999).</blockquote> <blockquote>In this paper, we propose using empirical likelihood in survey sampling based on the so-called population empirical likelihood (POEL). In the POEL approach, a single empirical likelihood is defined for the finite population. The sampling design can be incorporated into the constraint in the optimization of the POEL. For some special sampling designs, the proposed method leads to optimal estimation and does not require artificial adjustment for constructing likelihood ratio confidence intervals. Furthermore, because a single empirical likelihood is defined for the finite population, it naturally incorporates auxiliary information obtained from multiple surveys. Results from two simulation studies are presented to show the finite sample performance of the proposed method.</blockquote> </blockquote></p>
dc.description.comments <p>This article is published as Chen, S. and Kim, J.K. (2014). “Population empirical likelihood for nonparametric inference in survey sampling,” <em>Statistica Sinica</em> 24, 335–355. doi:<a href="http://dx.doi.org/10.5705/ss.2011.294" target="_blank">10.5705/ss.2011.294</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/110/
dc.identifier.articleid 1111
dc.identifier.contextkey 10456611
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/110
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90411
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/110/2014_Kim_PopulationEmpirical.pdf|||Fri Jan 14 18:40:06 UTC 2022
dc.source.uri 10.5705/ss.2011.294
dc.subject.disciplines Design of Experiments and Sample Surveys
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Statistics and Probability
dc.subject.keywords Calibration estimation
dc.subject.keywords optimal estimation
dc.subject.keywords regression estimation
dc.subject.keywords Wilk's theorem
dc.title Population empirical likelihood for nonparametric inference in survey sampling
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication fdf914ae-e48d-4f4e-bfa2-df7a755320f4
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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