A Bayesian Large Deviations Probabilistic Interpretation and Justification of Empirical Likelihood

Marian Grendar and George Judge
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Marian Grendar: Bel University
George Judge: University of California, Berkeley and Giannini Foundation

No 1035, 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 (search for similar items in EconPapers)
Date: 2007-04-23
Note: oai:cdlib1:are_ucb-1136
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Persistent link: http://EconPapers.repec.org/RePEc:cdl:agrebk:1035

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