| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2014: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Outline of Final Research Achievements |
In this project, I developed the theory for analysis of missing data and applied it to special data in the discriminant analysis. As theoretical study research, I derived conditional independences equivalent to MAR(missing at random) under monotonic and non-monotonic missing-data mechanisms. In addition, I constructed a method to overcome some difficulties in computation and estimation of the parameters of interest with missing data by using the selection matrix. It allows us to investigate properties of the estimator which are necessary for inference. As application research, I tackled a semi-supervised learning problem in discriminant analysis using the missing-data analysis theory. The semi-supervised learning is an estimation of the parameters from the partially observed data. I showed that the use of the missing-data analysis theory makes it possible to obtain the correct discriminant rule even with the such partially observed data.
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