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Entropy 2012, 14(8), 1306-1316; doi:10.3390/e14081306

A New Entropy Optimization Model for Graduation of Data in Survival Analysis

1,2, 1,*  and 3
Received: 7 March 2012 / Revised: 6 June 2012 / Accepted: 4 July 2012 / Published: 25 July 2012
(This article belongs to the Special Issue Concepts of Entropy and Their Applications)
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Graduation of data is of great importance in survival analysis. Smoothness and goodness of fit are two fundamental requirements in graduation. Based on the instinctive defining expression for entropy in terms of a probability distribution, two optimization models based on the Maximum Entropy Principle (MaxEnt) and Minimum Cross Entropy Principle (MinCEnt) to estimate mortality probability distributions are presented. The results demonstrate that the two approaches achieve the two basic requirements of data graduating, smoothness and goodness of fit respectively. Then, in order to achieve a compromise between these requirements, a new entropy optimization model is proposed by defining a hybrid objective function combining both principles of MaxEnt and MinCEnt models linked by a given adjustment factor which reflects the preference of smoothness and goodness of fit in the data graduation. The proposed approach is feasible and more reasonable in data graduation when both smoothness and goodness of fit are concerned.
Keywords: entropy optimization; survival analysis; graduation of data entropy optimization; survival analysis; graduation of data
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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He, D.; Huang, Q.; Gao, J. A New Entropy Optimization Model for Graduation of Data in Survival Analysis. Entropy 2012, 14, 1306-1316.

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