RESEARCH ON PARAMETER ESTIMATION FOR SMALL SAMPLE CENSORED DATA

摘要

It is difficult to identify distribution types and to estimate parameters of the distribution for small sample censored data. 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. The algorithm of training based on SVM and the RBF kernel function was selected firstly; 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 constructed finally. The model was verified with Monte Carlo simulation method. Plenty of combinations of the numbers of training and testing data were processed to find the optimization of identification model and make it efficient. The results indicate that the new algorithm has more preferable performance in distribution type identification and parameter estimation than the traditional methods.