2013 Fiscal Year Final Research Report
Statistical inference involving integration and optimization
| Project/Area Number |
24700281
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Statistical science
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| Research Institution | Keio University |
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Principal Investigator |
SEI Tomonari 慶應義塾大学, 理工学部, 講師 (20401242)
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| Project Period (FY) |
2012-04-01 – 2014-03-31
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| Keywords | 統計数学 / 最適化 / ホロノミック勾配法 / 不均衡データ / 情報幾何 |
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| Research Abstract |
In statistics, the maximum likelihood estimation sometimes needs heavy computation due to the normalizing constant. The holonomic gradient methods are developed to avoid such computation and progress in recent years. In this research, the holonomic gradient methods are shown to be available for the Bingham distributions.
Behavior of the binomial regression model for an imbalanced dataset is also investigated. The imbalanced data means that the frequency of the binary label that should be classified is imbalanced. In this research, the limit of the model for such datasets is a special class of Poisson point processes.
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