Accurate methods for incomplete data analysis using the mixed trunsored model and the decision tree.

本项目的目标是通过估计模型参数和检验假设来分析数据由非均匀数据组成的不完全数据情况。为此，我们首先尝试将数据挖掘方案与不完全数据分析方法相结合，证明了混合Trun尾模型在医学领域或可靠性领域具有广泛的适用性。IEEE会刊论文被日本IEEE可靠性学会授予，说明该论文的研究水平得到了高度评价，并用算法给出了对杂乱数据进行分类的凸起搜索法，并给出了市场营销中的数值例子。我们已经在IEEE、IFORS和HICSS等方面做了多次介绍，并结合电气绝缘应用的一个具体实例，提出了截断模型和决策树模型的组合方法，从而成功地达到了我们提出的目标。

The objective of this project is to analyze the incomplete data cases where the data are consist of inhomogeneous data by estimating the model parameters and testing the hypotheses. To do this, first we tried to combine the data mining scheme with the incomplete data analysis methods.We have shown that the mixed trunsored model has applicability to many fields such as the medical area or the reliability fields. The fact that the paper of IEEE Transaction was awarded by Japanese IEEE Reliability Society shows that the research level is highly evaluated.Also, the bump hunting method, which is useful to classify the messy data, has been shown with algorithms, and the numerical examples in marketing have been illustrated. We have had many presentations in IEEE, IFORS, and HICSS etc.The combined method of the trunsored model and the decision tree model has been developed with a concrete example in electrical insulation applications.Thus, we have reached the goal we posed successfully.