A New Entropy Optimization Model for Graduation of Data in Survival Analysis
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 Princip...
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MDPI AG
2012-07-01
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Online Access: | http://www.mdpi.com/1099-4300/14/8/1306 |
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doaj-8ee0d814887c48f5bf976b13cbe3bd712020-11-25T01:39:55ZengMDPI AGEntropy1099-43002012-07-011481306131610.3390/e14081306A New Entropy Optimization Model for Graduation of Data in Survival AnalysisQi HuangJianwei GaoDayi HeGraduation 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.http://www.mdpi.com/1099-4300/14/8/1306entropy optimizationsurvival analysisgraduation of data |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qi Huang Jianwei Gao Dayi He |
spellingShingle |
Qi Huang Jianwei Gao Dayi He A New Entropy Optimization Model for Graduation of Data in Survival Analysis Entropy entropy optimization survival analysis graduation of data |
author_facet |
Qi Huang Jianwei Gao Dayi He |
author_sort |
Qi Huang |
title |
A New Entropy Optimization Model for Graduation of Data in Survival Analysis |
title_short |
A New Entropy Optimization Model for Graduation of Data in Survival Analysis |
title_full |
A New Entropy Optimization Model for Graduation of Data in Survival Analysis |
title_fullStr |
A New Entropy Optimization Model for Graduation of Data in Survival Analysis |
title_full_unstemmed |
A New Entropy Optimization Model for Graduation of Data in Survival Analysis |
title_sort |
new entropy optimization model for graduation of data in survival analysis |
publisher |
MDPI AG |
series |
Entropy |
issn |
1099-4300 |
publishDate |
2012-07-01 |
description |
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. |
topic |
entropy optimization survival analysis graduation of data |
url |
http://www.mdpi.com/1099-4300/14/8/1306 |
work_keys_str_mv |
AT qihuang anewentropyoptimizationmodelforgraduationofdatainsurvivalanalysis AT jianweigao anewentropyoptimizationmodelforgraduationofdatainsurvivalanalysis AT dayihe anewentropyoptimizationmodelforgraduationofdatainsurvivalanalysis AT qihuang newentropyoptimizationmodelforgraduationofdatainsurvivalanalysis AT jianweigao newentropyoptimizationmodelforgraduationofdatainsurvivalanalysis AT dayihe newentropyoptimizationmodelforgraduationofdatainsurvivalanalysis |
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1725048359975124992 |