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Open AccessArticle

Entropy and Divergence Associated with Power Function and the Statistical Application

The Institute of Statistical Mathematics, Tachikawa, Tokyo 190-8562, Japan
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Entropy 2010, 12(2), 262-274; https://doi.org/10.3390/e12020262
Received: 29 December 2009 / Revised: 20 February 2010 / Accepted: 23 February 2010 / Published: 25 February 2010
(This article belongs to the Special Issue Distance in Information and Statistical Physics Volume 2)
In statistical physics, Boltzmann-Shannon entropy provides good understanding for the equilibrium states of a number of phenomena. In statistics, the entropy corresponds to the maximum likelihood method, in which Kullback-Leibler divergence connects Boltzmann-Shannon entropy and the expected log-likelihood function. The maximum likelihood estimation has been supported for the optimal performance, which is known to be easily broken down in the presence of a small degree of model uncertainty. To deal with this problem, a new statistical method, closely related to Tsallis entropy, is proposed and shown to be robust for outliers, and we discuss a local learning property associated with the method. View Full-Text
Keywords: Tsallis entropy; projective power divergence; robustness Tsallis entropy; projective power divergence; robustness
MDPI and ACS Style

Eguchi, S.; Kato, S. Entropy and Divergence Associated with Power Function and the Statistical Application. Entropy 2010, 12, 262-274. https://doi.org/10.3390/e12020262

AMA Style

Eguchi S, Kato S. Entropy and Divergence Associated with Power Function and the Statistical Application. Entropy. 2010; 12(2):262-274. https://doi.org/10.3390/e12020262

Chicago/Turabian Style

Eguchi, Shinto; Kato, Shogo. 2010. "Entropy and Divergence Associated with Power Function and the Statistical Application" Entropy 12, no. 2: 262-274. https://doi.org/10.3390/e12020262

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