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Entropy 2009, 11(4), 917-930; doi:10.3390/e11040917

A Weighted Generalized Maximum Entropy Estimator with a Data-driven Weight

Department of Agricultural Economics, Texas A&M University, College Station, TX 77843-2124, USA
Received: 24 September 2009 / Accepted: 16 November 2009 / Published: 26 November 2009
(This article belongs to the Special Issue Maximum Entropy)
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The method of Generalized Maximum Entropy (GME), proposed in Golan, Judge and Miller (1996), is an information-theoretic approach that is robust to multicolinearity problem. It uses an objective function that is the sum of the entropies for coefficient distributions and disturbance distributions. This method can be generalized to the weighted GME (W-GME), where different weights are assigned to the two entropies in the objective function. We propose a data-driven method to select the weights in the entropy objective function. We use the least squares cross validation to derive the optimal weights. MonteCarlo simulations demonstrate that the proposedW-GME estimator is comparable to and often outperforms the conventional GME estimator, which places equal weights on the entropies of coefficient and disturbance distributions.
Keywords: maximum entropy; generalized maximum entropy method; cross validation maximum entropy; generalized maximum entropy method; cross validation
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Wu, X. A Weighted Generalized Maximum Entropy Estimator with a Data-driven Weight. Entropy 2009, 11, 917-930.

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