Next Article in Journal
Equivalence of Partition Functions Leads to Classification of Entropies and Means
Next Article in Special Issue
Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review
Previous Article in Journal
A Model of Nonsingular Universe
Previous Article in Special Issue
Socio-Thermodynamics—Evolutionary Potentials in a Population of Hawks and Doves
Article Menu

Export Article

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

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

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.
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)
View Full-Text   |   Download PDF [649 KB, uploaded 24 February 2015]   |  

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.
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 (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

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

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