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Econometrics 2015, 3(2), 199-214; doi:10.3390/econometrics3020199

Plug-in Bandwidth Selection for Kernel Density Estimation with Discrete Data

Department of Economics, Finance, and Legal Studies, University of Alabama, Tuscaloosa, AL 35487-0224, USA
Department of Economics, University of Miami, Coral Gables, FL 33124-6520, USA
Author to whom correspondence should be addressed.
Received: 27 November 2014 / Revised: 2 March 2015 / Accepted: 11 March 2015 / Published: 31 March 2015
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This paper proposes plug-in bandwidth selection for kernel density estimation with discrete data via minimization of mean summed square error. Simulation results show that the plug-in bandwidths perform well, relative to cross-validated bandwidths, in non-uniform designs. We further find that plug-in bandwidths are relatively small. Several empirical examples show that the plug-in bandwidths are typically similar in magnitude to their cross-validated counterparts. View Full-Text
Keywords: nonparametric; kernel; discrete variable; bandwidth selection; plug-in nonparametric; kernel; discrete variable; bandwidth selection; plug-in

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chu, C.-Y.; Henderson, D.J.; Parmeter, C.F. Plug-in Bandwidth Selection for Kernel Density Estimation with Discrete Data. Econometrics 2015, 3, 199-214.

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