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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
Abstract: 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
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Cite This Article
MDPI and ACS Style
Wu, X. A Weighted Generalized Maximum Entropy Estimator with a Data-driven Weight. Entropy 2009, 11, 917-930.
Wu X. A Weighted Generalized Maximum Entropy Estimator with a Data-driven Weight. Entropy. 2009; 11(4):917-930.
Wu, Ximing. 2009. "A Weighted Generalized Maximum Entropy Estimator with a Data-driven Weight." Entropy 11, no. 4: 917-930.