Large Deviations Theory and Empirical Estimator Choice

Marian Grendar and George Judge ()

Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley

Abstract: Criterion choice is such a hard problem in information recovery and in estimation and inference. In the case of inverse problems with noise, can probabilistic laws provide a basis for empirical estimator choice? That is the problem we investigate in this paper. Large Deviations Theory is used to evaluate the choice of estimator in the case of two fundamental situations-problems in modelling data. The probabilistic laws developed demonstrate that each problem has a unique solution-empirical estimator. Whether other members of the empirical estimator family can be associated a particular problem and conditional limit theorem, is an open question.

Keywords: entropy; information theory; large deviations; empirical likelihood; Boltzmann Jaynes Inverse Problem; probabilistic laws; Social and Behavioral Sciences (search for similar items in EconPapers)
Date: 2006-01-01
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Working Paper: Large Deviations Theory and Empirical Estimator Choice (2006)
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