Statistical inference under non-regular conditions by the likelihood ratio approach and its applications.
| Project/Area Number |
21700320
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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 | The Institute of Statistical Mathematics (2010)
Transdisciplinary Research Integration Center (2009) |
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Principal Investigator |
FUJII Takayuki The Institute of Statistical Mathematics, リスク解析戦略研究センター, 特任研究員 (40530259)
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| Project Period (FY) |
2009 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2010: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2009: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | 統計的推測 / 非正則モデル / 尤度比統計量 / 最尤推定量 / ベイズ推定量 / 確率過程 / 漸近有効性 / 単純自己修正点過程 / ストレス解放モデル / 最尤推定 / ベイズ推定 / 回帰分析 |
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| Research Abstract |
Without regularity conditions which are often assumed in common statistical inference, several parametric estimation problems are considered. The likelihood ratio approach is well-known in singular estimation problems such as the cusp estimation. In this study, this method is applied in the regression model that is quite important in applied statistics. Also, some non-regular estimation problems are studied in parametric models of stochastic process.
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Report
(3 results)
Research Products
(9 results)
