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

该项目的目的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整数据情况。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。结果表明，混合截数模型可应用于许多领域，如医学领域和可靠性领域。《IEEE Transaction》论文获得日本IEEE可靠性学会的奖项，表明了该论文的研究水平得到了高度评价。此外，本文还用算法说明了对杂乱数据进行分类的凸点搜索方法，并对市场营销中的数值例子进行了说明。我们在IEEE、IFORS、HICSS等会议上做了多次报告，并提出了截断模型与决策树模型相结合的方法，并给出了具体的电绝缘应用实例。因此，我们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.