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Bayesian analysis of restricted penalized empirical likelihood

Author

Listed:
  • Mahdieh Bayati

    (Islamic Azad University)

  • Seyed Kamran Ghoreishi

    (University of Qom)

  • Jingjing Wu

    (University of Calgary)

Abstract

In this paper, we introduce restricted empirical likelihood and restricted penalized empirical likelihood estimators. These estimators are obtained under both unbiasedness and minimum variance criteria for estimating equations. These scopes produce estimators which have appealing properties and particularly are more robust against outliers than some currently existing estimators. Assuming some prior densities, we develop the Bayesian analysis of the restricted empirical likelihood and the restricted penalized empirical likelihood. Moreover, we provide an EM algorithm to approximate hyper-parameters. Finally, we carry out a simulation study and illustrate the theoretical results for a real data set.

Suggested Citation

  • Mahdieh Bayati & Seyed Kamran Ghoreishi & Jingjing Wu, 2021. "Bayesian analysis of restricted penalized empirical likelihood," Computational Statistics, Springer, vol. 36(2), pages 1321-1339, June.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:2:d:10.1007_s00180-020-01046-3
    DOI: 10.1007/s00180-020-01046-3
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    References listed on IDEAS

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