Please use this identifier to cite or link to this item: https://doi.org/10.1093/biomet/asr004
Title: Empirical likelihood for small area estimation
Authors: Chaudhuri, S. 
Ghosh, M.
Keywords: Area level model
Dirichlet process mixture prior
Exponential tilting
Hierarchical Gaussian prior
Unit level model
Issue Date: Jun-2011
Citation: Chaudhuri, 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
Abstract: 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. © 2011 Biometrika Trust.
Source Title: Biometrika
URI: http://scholarbank.nus.edu.sg/handle/10635/105118
ISSN: 00063444
DOI: 10.1093/biomet/asr004
Appears in Collections:Staff Publications

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