Please use this identifier to cite or link to this item: https://doi.org/10.1093/biomet/asr004
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dc.titleEmpirical likelihood for small area estimation
dc.contributor.authorChaudhuri, S.
dc.contributor.authorGhosh, M.
dc.date.accessioned2014-10-28T05:11:47Z
dc.date.available2014-10-28T05:11:47Z
dc.date.issued2011-06
dc.identifier.citationChaudhuri, S., Ghosh, M. (2011-06). Empirical likelihood for small area estimation. Biometrika 98 (2) : 473-480. ScholarBank@NUS Repository. https://doi.org/10.1093/biomet/asr004
dc.identifier.issn00063444
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105118
dc.description.abstractCurrent methodologies in small area estimation are mostly either parametric or heavily dependent on the assumed linearity of the estimators of the small area means. We discuss an alternative empirical likelihood-based Bayesian approach, which neither requires a parametric likelihood nor assumes linearity of the estimators, and can handle both discrete and continuous data in a unified manner. Empirical likelihoods for both area- and unit-level models are introduced. We discuss the suitability of the proposed likelihoods in Bayesian inference and illustrate their performances on a real dataset and a simulation study. © 2011 Biometrika Trust.
dc.sourceScopus
dc.subjectArea level model
dc.subjectDirichlet process mixture prior
dc.subjectExponential tilting
dc.subjectHierarchical Gaussian prior
dc.subjectUnit level model
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1093/biomet/asr004
dc.description.sourcetitleBiometrika
dc.description.volume98
dc.description.issue2
dc.description.page473-480
dc.description.codenBIOKA
dc.identifier.isiut000291063300016
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