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Empirical likelihood for small area estimation

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  • Sanjay Chaudhuri
  • Malay Ghosh

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. Copyright 2011, Oxford University Press.

Suggested Citation

  • Sanjay Chaudhuri & Malay Ghosh, 2011. "Empirical likelihood for small area estimation," Biometrika, Biometrika Trust, vol. 98(2), pages 473-480.
    • Handle: RePEc:oup:biomet:v:98:y:2011:i:2:p:473-480
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    File URL: http://hdl.handle.net/10.1093/biomet/asr004
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    References listed on IDEAS

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    Cited by:

    1. Sugasawa, Shonosuke & Kubokawa, Tatsuya, 2015. "Parametric transformed Fay–Herriot model for small area estimation," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 295-311.
    2. Bedoui, Adel & Lazar, Nicole A., 2020. "Bayesian empirical likelihood for ridge and lasso regressions," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    3. Liu Yang & Nandram Balgobin, 2022. "Sampling methods for the concentration parameter and discrete baseline of the Dirichlet Process," Statistics in Transition New Series, Polish Statistical Association, vol. 23(4), pages 21-36, December.
    4. Kai Yang & Xue Ding & Xiaohui Yuan, 2022. "Bayesian empirical likelihood inference and order shrinkage for autoregressive models," Statistical Papers, Springer, vol. 63(1), pages 97-121, February.
    5. Sanjay Chaudhuri & Debashis Mondal & Teng Yin, 2017. "Hamiltonian Monte Carlo sampling in Bayesian empirical likelihood computation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 293-320, January.
    6. Siddharta Chib & Minchul Shin & Anna Simoni, 2016. "Bayesian Empirical Likelihood Estimation and Comparison of Moment Condition Models," Working Papers 2016-21, Center for Research in Economics and Statistics.
    7. Ouyang, Jiangrong & Bondell, Howard, 2023. "Bayesian analysis of longitudinal data via empirical likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    8. Vexler, Albert & Zou, Li & Hutson, Alan D., 2019. "The empirical likelihood prior applied to bias reduction of general estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 96-106.

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