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首页> 外文会议>1st international conference on transportation information and safety 2011.;vol. 1. >RESEARCH ON PARAMETER ESTIMATION FOR SMALL SAMPLE CENSORED DATA
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RESEARCH ON PARAMETER ESTIMATION FOR SMALL SAMPLE CENSORED DATA

机译：小样本删失数据的参数估计研究

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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.
机译：对于小样本审查数据，很难识别分布类型和估计分布参数。基于统计学习理论和支持向量机（SVM）的多元素分类器算法，建立了智能的分布识别模型，并将其应用于小样本删失数据的参数估计，以提高传统方法的精度。首先选择了基于支持向量机和RBF核函数的训练算法；其次，绘制了分布特征参数。在此基础上，最终建立了分布辨识模型和参数估计模型。通过蒙特卡洛仿真方法对该模型进行了验证。处理了大量训练和测试数据的组合，以找到优化的识别模型并使其高效。结果表明，与传统方法相比，新算法在配电类型识别和参数估计方面具有更好的性能。

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