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A Bayesian Large Deviations Probabilistic Interpretation and Justification of Empirical Likelihood

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  • Grendar, Marian
  • Judge, George G.

Abstract

In this paper we demonstrate, in a parametric Estimating Equations setting, that the Empirical Likelihood (EL) method is an asymptotic instance of the Bayesian non-parametric Maximum-A-Posteriori approach. The resulting probabilistic interpretation and justifcation of EL rests on Bayesian non-parametric consistency in L-divergence.

Suggested Citation

  • Grendar, Marian & Judge, George G., 2007. "A Bayesian Large Deviations Probabilistic Interpretation and Justification of Empirical Likelihood," CUDARE Working Papers 7191, University of California, Berkeley, Department of Agricultural and Resource Economics.
    • Handle: RePEc:ags:ucbecw:7191
    DOI: 10.22004/ag.econ.7191
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    Cited by:

    1. Grendar, Marian & Judge, George G. & Niven, R. K., 2007. "Large Deviations Approach to Bayesian Nonparametric Consistency: the Case of Polya Urn Sampling," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt2s97t5km, Department of Agricultural & Resource Economics, UC Berkeley.
    2. Grendar, Marian & Judge, George G. & Niven, R. K., 2007. "Large Deviations Approach to Bayesian Nonparametric Consistency: the Case of Polya Urn Sampling," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt2s97t5km, Department of Agricultural & Resource Economics, UC Berkeley.

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    Keywords

    Research Methods/ Statistical Methods;

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