A Bayesian Large Deviations Probabilistic Interpretation and Justification of Empirical Likelihood
Marian Grendar and
George Judge ()
Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley
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: Maximum Non-parametric Likelihood; Estimating Equations; Bayesian non-parametric consistency; Sanov Theorem for Sampling Distributions; L-divergence; Social and Behavioral Sciences (search for similar items in EconPapers)
Date: 2007-04-23
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Citations: View citations in EconPapers (1)
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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:cdl:agrebk:qt1z012014
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