Bayesian Analysis of Covariance Structure Model to Identify Causal Relationships
| Project/Area Number |
10680314
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | The University of Tokyo |
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Principal Investigator |
SHIGEMASU Kazuo The University of Tokyo, Graduate School of Arts and Sciences, Professor, 大学院・総合文化研究科, 教授 (90091701)
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| Co-Investigator(Kenkyū-buntansha) |
MAYEKAWA Shin-ichi The National Center for University Entrance Examinations, Associate Professor, 大学入試センター, 助教授 (70190288)
能智 正博 東京大学, 総合文化研究科, 助手 (30292717)
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| Project Period (FY) |
1998 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 1999: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1998: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Keywords | Bayesian Approach / Posterior Distribution / Gibbs Sampler / Structural Equation Model / Information Criterion / Model Selection / 尤度比 / 適合度 / 事後確率 |
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| Research Abstract |
The joint posterior distributions for all relevant parameters in covariance structure model are derived. Further, full conditional distributions for each parameter set with the rest of parameter given are derived Then, based on these conditional distributions, we used Gibbs Sampler to derive numerically the marginal distributions for interested parameters.. This procedure was applied successfully to both simulated data and real data.
We also proposed the new Bayesian criterion to choose the best covariance structure model. This criterion is the approximation of the posterior probability that each model is true. Using simulated data, we compared the performance of this new criterion with the established information criteria and goodness of fit indexes. The results showed that the new criterion succeeded in identifying the true model better than any other criteria.
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Report
(3 results)
Research Products
(16 results)
