Next Article in Journal
Maximum Entropy Production as an Inference Algorithm that Translates Physical Assumptions into Macroscopic Predictions: Don’t Shoot the Messenger
Next Article in Special Issue
Fisher Information and Semiclassical Treatments
Previous Article in Journal
On the Structural Non-identifiability of Flexible Branched Polymers
Previous Article in Special Issue
Maximum Entropy Estimation of Transition Probabilities of Reversible Markov Chains
Article Menu

Export Article

Open AccessArticle
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)
View Full-Text   |   Download PDF [189 KB, uploaded 24 February 2015]

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 maximum entropy; generalized maximum entropy method; cross validation
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & 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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top