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  Empirical Bayes Nonparametric Kernel Density Estimation
Alan P. Ker and A. Tolga Ergün
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  Abstract
 
This manuscript proposes using empirical Bayes techniques on estimated density values from nonparametric kernels in attempts to exploit potential similarities among a set of unknown densities. Our asymptotic theory and simulation results suggest that the emipirical Bayes nonparamtetric kernel estimator may be a viable alternative to the standard kernel estimator when a set of possibly similar densities are being estimated. The strengths of the proposed estimator are (i) it allows all types of kernel estimators; and (ii) it does not require specification as to the degree or form of similarity.

 

 
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2011 Dept. of Agricultural & Resource Economics, The University of Arizona
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Last updated October 13, 2004
Document located at http://ag.arizona.edu/arec/pubs/researchpapers/abstract2004-05.html