Parameter Estimation for Small Sample Censored Data Based on SVM


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It is difficult to identify distribution types and to estimate parameters of the distribution for small sample censored data when you deal with mechanical equipment reliability analysis. Here, an intelligent distribution identification model was established based on statistical learning theory and the algorithm of multi-element classifier of Support Vector Machine (SVM), and also applied to parameter estimation of small sample censored data, in order to improve the precision of traditional method. Firstly, the algorithm of training based on SVM and the RBF kernel function was selected; secondly, the parameters of the distributions characteristics were drawn; on the basis of these conditions, the distributions identification model and the parameter estimation model were finally constructed. And the model was verified with Monte Carlo simulation method. The results indicate that the new algorithm has more preferable performance in distribution type identification and parameter estimation than the traditional methods.



Edited by:

Qingxue Huang, Cunlong Zhou, Zhengyi Jiang, Jianmei Wang, Hailian Gui, Lifeng Ma, Lidong Ma, Yugui Li and Chunjiang Zhao




Y. Fan et al., "Parameter Estimation for Small Sample Censored Data Based on SVM", Advanced Materials Research, Vol. 145, pp. 31-36, 2011

Online since:

October 2010




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