Outliers Data Mining in Normal-Inverse Gaussian Model

机译：普通逆高斯模型中的异常值数据挖掘

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摘要

The normal-inverse model arises as a normal variance-mean mixture with an inverse Gaussian mixing model. The resulting model, it is very complicated to obtain the influence measures based on the tradition method. In the present paper, several diagnostic measures for outlier data mining are obtained based on the conditional expectation of the complete-data log-likelihood function based on the EM algorithm. An example for which we apply the diagnosis methods is given as illustration.

机译：正常逆模型产生与具有逆高斯混合模型的正常方差 - 平均混合物。得到的模型，基于传统方法获得影响措施非常复杂。在本文中，基于基于EM算法的完整数据记录似然函数的条件期望获得了几种对异常数据挖掘的诊断措施。我们应用诊断方法的一个例子是作为图示。

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《International Conference on Information and Computing》|2010年||共4页
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作者
Li-li Wang; Xiang-yang Hou; Yan-ye Xiong;
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中图分类 G20-53;
关键词
EM algorithm; Generalized Cook distance; Local influence analysis; Normal inverse Gaussian model;

机译：EM算法;普遍的厨师距离;局部影响分析;正常的逆高斯模型;

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4. Outliers Data Mining in Normal-Inverse Gaussian Model [C] . Li-li Wang, Xiang-yang Hou, Yan-ye Xiong International Conference on Information and Computing . 2010

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Outliers Data Mining in Normal-Inverse Gaussian Model

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