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Evaluation of spatial Bayesian Empirical Likelihood models in analysis of small area data

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  • Farzana Jahan
  • Daniel W Kennedy
  • Earl W Duncan
  • Kerrie L Mengersen

Abstract

Bayesian empirical likelihood (BEL) models are becoming increasingly popular as an attractive alternative to fully parametric models. However, they have only recently been applied to spatial data analysis for small area estimation. This study considers the development of spatial BEL models using two popular conditional autoregressive (CAR) priors, namely BYM and Leroux priors. The performance of the proposed models is compared with their parametric counterparts and with existing spatial BEL models using independent Gaussian priors and generalised Moran basis priors. The models are applied to two benchmark spatial datasets, simulation study and COVID-19 data. The results indicate promising opportunities for these models to capture new insights into spatial data. Specifically, the spatial BEL models outperform the parametric spatial models when the underlying distributional assumptions of data appear to be violated.

Suggested Citation

  • Farzana Jahan & Daniel W Kennedy & Earl W Duncan & Kerrie L Mengersen, 2022. "Evaluation of spatial Bayesian Empirical Likelihood models in analysis of small area data," PLOS ONE, Public Library of Science, vol. 17(5), pages 1-27, May.
    • Handle: RePEc:plo:pone00:0268130
    DOI: 10.1371/journal.pone.0268130
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    References listed on IDEAS

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    1. Siddhartha Chib & Minchul Shin & Anna Simoni, 2018. "Bayesian Estimation and Comparison of Moment Condition Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1656-1668, October.
    2. Kang, Emily L. & Cressie, Noel, 2011. "Bayesian Inference for the Spatial Random Effects Model," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 972-983.
    3. Sanjay Chaudhuri & Malay Ghosh, 2011. "Empirical likelihood for small area estimation," Biometrika, Biometrika Trust, vol. 98(2), pages 473-480.
    4. Bedoui, Adel & Lazar, Nicole A., 2020. "Bayesian empirical likelihood for ridge and lasso regressions," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    5. Song Chen & Ingrid Van Keilegom, 2009. "A review on empirical likelihood methods for regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(3), pages 415-447, November.
    6. Puying Zhao & Malay Ghosh & J. N. K. Rao & Changbao Wu, 2020. "Bayesian empirical likelihood inference with complex survey data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(1), pages 155-174, February.
    7. Aritra Sengupta & Noel Cressie, 2013. "Empirical Hierarchical Modelling for Count Data using the Spatial Random Effects Model," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 389-418, September.
    8. Wang Q. & Linton O. & Hardle W., 2004. "Semiparametric Regression Analysis With Missing Response at Random," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 334-345, January.
    9. Lee, Duncan, 2013. "CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i13).
    10. Nicole A. Lazar, 2003. "Bayesian empirical likelihood," Biometrika, Biometrika Trust, vol. 90(2), pages 319-326, June.
    11. Daniel Nordman, 2008. "An empirical likelihood method for spatial regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 68(3), pages 351-363, November.
    12. Susanne M. Schennach, 2005. "Bayesian exponentially tilted empirical likelihood," Biometrika, Biometrika Trust, vol. 92(1), pages 31-46, March.
    13. Nordman, Daniel J. & Caragea, Petruta C., 2008. "Point and Interval Estimation of Variogram Models Using Spatial Empirical Likelihood," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 350-361, March.
    14. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
    15. Wu, Rongning & Cao, Jiguo, 2011. "Blockwise empirical likelihood for time series of counts," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 661-673, March.
    16. Song Chen & Ingrid Van Keilegom, 2009. "Rejoinder on: A review on empirical likelihood methods for regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(3), pages 468-474, November.
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