A Bayesian Large Deviations Probabilistic Interpretation and Justification of Empirical Likelihood

Marian Grendar and George Judge (gjudge@berkeley.edu)

No 7191, CUDARE Working Papers from University of California, Berkeley, Department of Agricultural and Resource Economics

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.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 12
Date: 2007
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Citations: View citations in EconPapers (2)

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Related works:
Working Paper: A Bayesian Large Deviations Probabilistic Interpretation and Justification of Empirical Likelihood (2007)
Working Paper: A Bayesian large deviations probabilistic interpretation and justification of empirical likelihood (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:ucbecw:7191

DOI: 10.22004/ag.econ.7191

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