Parameter Estimation for Small Sample Censored data Based on SVM

摘要：

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.

关键词： Distribution Type Identification Parameter Estimation SVM Small Sample Censored Data Monte Carlo Simulation

作者: Ying Fan Shunkun Wang Feng Zhou Zhicheng Tian Guangshuai Ding

作者单位: School of Machinery & Electronics Engineering, Taiyuan University of Science & Technology,Taiyuan 03 College of Engineering, China Agricultural University. Beijing 100083, China

会议类型: 国际会议

会议名称: International Symposium on Advanced Rolling Equipment Technologies(第一届轧钢设备新技术国际研讨会 ISARET 2010)

会议地点: 太原

会议语种:英文

页码: 31-36

在线出版日期: 2010-09-23（万方平台首次上网日期，不代表论文的发表时间）