{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T21:38:33Z","timestamp":1790804313566,"version":"4.1.0"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Current 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.<\/jats:p>","DOI":"10.1093\/biomet\/asr004","type":"journal-article","created":{"date-parts":[[2011,5,24]],"date-time":"2011-05-24T15:29:04Z","timestamp":1306250944000},"page":"473-480","source":"Crossref","is-referenced-by-count":33,"title":["Empirical likelihood for small area estimation"],"prefix":"10.1093","volume":"98","author":[{"given":"Sanjay","family":"Chaudhuri","sequence":"first","affiliation":[{"name":"Department of Statistics and Applied Probability, National University of Singapore, 117546 Singapore, sanjay@stat.nus.edu.sg"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malay","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Florida, Gainesville, Florida 32611, U.S.A., ghoshm@stat.ufl.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2011,6,1]]},"reference":[{"key":"2023021511084993400_","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1093\/biomet\/89.1.230","article-title":"Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys","volume":"89","author":"Chen","year":"2002","journal-title":"Biometrika"},{"key":"2023021511084993400_","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1080\/00031305.1995.10476177","article-title":"Understanding the Metropolis-Hastings algorithm","volume":"49","author":"Chib","year":"1995","journal-title":"Am. 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