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An efficient algorithm to compute maximum entropy densities

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Author Info

  • D. Ormoneit
  • H. White

Abstract

We describe an algorithm to efficiently compute maximum entropy densities, i.e. densities maximizing the Shannon entropy - [image omitted]� under a set of constraints [image omitted]�. Our method is based on an algorithm by Zellner and Highfield, which has been found not to converge under a variety of circumstances. To demonstrate that our method overcomes these difficulties, we conduct numerous experiments for the special case gi(x) = xi, n = 4. An extensive table of results for this case and computer code are available on the World Wide Web

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 18 (1999)
Issue (Month): 2 ()
Pages: 127-140

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Handle: RePEc:taf:emetrv:v:18:y:1999:i:2:p:127-140

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Related research

Keywords: Density Estimation; Maximum Entropy Principle; Shannon Entropy; JEL Classification:C61; C63; C87;

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Cited by:
  1. Alberto HOLLY & Alain MONFORT & Michael ROCKINGER, 2011. "Fourth Order Pseudo Maximum Likelihood Methods," Working Papers 2011-05, Centre de Recherche en Economie et Statistique.
  2. Douglas Miller, 2007. "An Information Theoretic Approach to Flexible Stochastic Frontier Models," Working Papers 0717, Department of Economics, University of Missouri.
  3. Rockinger, M. & Jondeau, E., 2001. "Entropy Densities: with an Application to Autoregressive Conditional Skewness and Kurtosis," Working papers 79, Banque de France.
  4. Usta, Ilhan & Kantar, Yeliz Mert, 2011. "On the performance of the flexible maximum entropy distributions within partially adaptive estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2172-2182, June.
  5. Wu, Ximing, 2003. "Calculation of maximum entropy densities with application to income distribution," Journal of Econometrics, Elsevier, vol. 115(2), pages 347-354, August.
  6. Wu, Ximing & Perloff, Jeffrey M, 2004. "China's income distribution over time: reasons for rising inequality," CUDARE Working Paper Series 0977, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy.
  7. Thanasis Stengos & Ximing Wu, 2010. "Information-Theoretic Distribution Test with Application to Normality," Econometric Reviews, Taylor & Francis Journals, vol. 29(3), pages 307-329.
  8. Miller, Douglas J. & Liu, Wei-han, 2002. "On the recovery of joint distributions from limited information," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 259-274, March.

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