2013 Fiscal Year Final Research Report
Development of Some Multivariate Statistical Inference Procedures for Missing and High Dimensional Data and Its Application
| Project/Area Number |
23500360
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | Tokyo University of Science |
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Principal Investigator |
SEO Takashi 東京理科大学, 理学部, 教授 (00266909)
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| Co-Investigator(Renkei-kenkyūsha) |
SHUTOH Nobumichi 神戸大学, 大学院・海事科学研究科, 講師 (00634099)
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| Project Period (FY) |
2011 – 2013
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| Keywords | 統計数学 / 統計理論 / 多変量解析 / 漸近展開 |
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| Research Abstract |
We obtained some results for the tests of mean vectors when the data have missing observations. In particular, we gave the test procedures for mean vectors using the likelihood ratio test statistic and Hotelling type test statistic for the one sample and two sample problems when the data set has two-step monotone missing data. We also gave the simultaneous confidence intervals for any and all linear compounds of the mean. Further, the accuracy of the approximation is investigated by Monte Carlo simulation. Testing specified values for the mean vector and the covariance matrix with a two-step monotone missing data were considered and derived the likelihood ratio test statistic and some results. As for the study of high dimensional data, we obtained some results for the problem of discriminant analysis and testing for multivariate normality and so on.
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