A theoretical approach to Bayesian statistical inference by a special shrinkage prior distribution
| Project/Area Number |
21740065
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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 |
General mathematics (including Probability theory/Statistical mathematics)
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| Research Institution | The University of Tokyo |
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Principal Investigator |
MARUYAMA Yuzo The University of Tokyo, 空間情報科学研究センター, 准教授 (30304728)
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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 |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2010: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2009: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
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| Keywords | 数理統計学 / 統計科学 / 統計数学 / ベイズ理論 |
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| Research Abstract |
For the balanced ANOVA setup, we propose a new closed form Bayes factor without integral representation, which is however based on fully Bayes method, with reasonable model selection consistency for two asymptotic situations (either number of levels of the factor or number of replication in each level goes to infinity). Exact analytical calculation of the marginal density under a special choice of the priors enables such a Bayes factor. The most advantage of our Bayes factor over existing Bayes factors is its excellent closed form. Since many Bayes factors based on fully Bayes method involve the integral in the representation, they have to apply the Laplace approximation in practice. However, the answer to the question which type of the Laplace approximation is more appropriate, is obscure for some cases. On the other hand, our Bayes factor does not require thought and has a reasonable model selection consistency
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Report
(3 results)
Research Products
(17 results)
