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Bayesian analysis of restricted penalized empirical likelihood

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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.

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Author information

Authors and Affiliations

  1. Department of Statistics, Science and Research branch, Islamic Azad University, Tehran, Iran

    Mahdieh Bayati

  2. Department of Statistics, University of Qom, Qom, Iran

    Seyed Kamran Ghoreishi

  3. Department of Mathematics and Statistics, University of Calgary, Calgary, Canada

    Jingjing Wu

Authors
  1. Mahdieh Bayati
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  2. Seyed Kamran Ghoreishi
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  3. Jingjing Wu
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Correspondence to Seyed Kamran Ghoreishi.

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Bayati, M., Ghoreishi, S.K. & Wu, J. Bayesian analysis of restricted penalized empirical likelihood. Comput Stat 36, 1321–1339 (2021). https://doi.org/10.1007/s00180-020-01046-3

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  • DOI: https://doi.org/10.1007/s00180-020-01046-3

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