| Project/Area Number |
05680189
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Educational technology
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| Research Institution | The National Center for University Entrance Examinations |
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Principal Investigator |
MAEKAWA Shinichi The National Center for University Entrance Examinations. Research Division, Associate Professor, 研究開発部, 助教授 (70190288)
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| Co-Investigator(Kenkyū-buntansha) |
YAMADA Fumiyasu The National Center for University Entrance Examinations.Research Division, Asso, 研究開発部, 教授 (40158217)
SHIGEMASU Kazuo The Tokyo Institute of Technology Department of Engeneering Professor, 工学部, 教授 (90091701)
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| Project Period (FY) |
1993 – 1994
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| Project Status |
Completed (Fiscal Year 1994)
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| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1994: ¥400,000 (Direct Cost: ¥400,000)
Fiscal Year 1993: ¥1,600,000 (Direct Cost: ¥1,600,000)
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| Keywords | m-group regression / neural network regression / utility scaling / 経験的ベイズ推定法 |
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| Research Abstract |
M-group regression analysis technique so far developed has two major problems. The first one is that the model always assumes the exchangeability of all the regression coefficients included in the model. In this research, we developped a general method which can handle a mixture of 1) common regression coefficients across groups, 2) exchangeable regression coefficients across groups, and 3) non-exchangeable regression coefficients. A computer program based on this model was developped.
The second problem is that the usual in-group regression analysis is restricted to the linear model. In this research, we investigated the possibility of extending the original idea to non-linear models including spline model and neural network model. As the first step, an efficient algorithm for neural netrowk regression was developped. We found that developping the m-group version of the newral network regression models seems promissing.
As for measuring utility functions, a computer program was developped which uses the maximum likelihood estimation technique based on the assumption that the indifference probabilities have beta distribution.
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