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1

School of Humanities & Economics Management, Chinese University of Geosciences, Number 29, Xueyuan Rd., Haidian, Beijing 100083, China

2

Lab of Resources & Environment Management, Chinese University of Geosciences, Number 29, Xueyuan Rd., Haidian, Beijing 100083, China

3

School of Business Administration, North China Electronic Power University, Number 2, Beinong Rd., Changpin, Beijing 102206, China





*

Author to whom correspondence should be addressed.



Abstract 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. View Full-Text

Keywords: entropy optimization; survival analysis; graduation of data entropy optimization; survival analysis; graduation of data





Autor: Dayi He 1,2, Qi Huang 1,* and Jianwei Gao 3

Fuente: http://mdpi.com/



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