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Large-Deviations Theory and Empirical Estimator Choice


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  • Marian Grendar
  • George Judge


In this article, we consider the problem of criterion choice in information recovery and inference in a large-deviations (LD) context. Kitamura and Stutzer recognize that the Maximum Entropy Empirical Likelihood estimator can be given a LD justification (Kitamura and Stutzer, 2002). We demonstrate there exists a similar LD justification for Owen's Empirical Likelihood estimator (Owen, 2001). We tie the two empirical estimators and related LD theorems to two basic ill-posed inverse problems α and β. We note that other estimators in this family lack an LD footing and provide an extensive discussion of the implications of these results. The appendix contains formal statements regarding relevant LD theorems.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 27 (2008)
Issue (Month): 4-6 ()
Pages: 513-525

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Handle: RePEc:taf:emetrv:v:27:y:2008:i:4-6:p:513-525

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Keywords: Boltzmann Jaynes inverse problem; Criterion choice problem; Empirical likelihood; Entropy; Information theory; Large deviations; Probabilistic laws;


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