2010 Fiscal Year Final Research Report
Model Selection for Singular Statistical Models and its Application
| Project/Area Number |
20700252
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Statistical science
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| Research Institution | Kyushu University |
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Principal Investigator |
NINOMIYA Yoshiyuki Kyushu University, 大学院・数理学研究院, 准教授 (50343330)
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| Project Period (FY) |
2008 – 2010
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| Keywords | 計量経済学 / 計量心理学 / 情報量規準 / 漸近分布論 / 統計幾何学 / 非正則性 / モデル選択 / 尤度比 |
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| Research Abstract |
Model selection is an indispensable task in statistical analysis. It is a method to select the optimal model from data in several model candidates. To use AIC is one of the most basic ideas for such model selection. The AIC is an estimator of the distance between the true distribution and the estimated distribution in a model candidate, and so the model which gives the minimum AIC is regarded as the optimal model. For so-called singular statistical models, however, any reasonable AIC has not derived until now. The achievement of this research is to derive a reasonable AIC for a structural change model and a factor analysis model, which are examples of the singular statistical models.
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Research Products
(9 results)
