| Project/Area Number |
12680318
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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 | Nagasaki University |
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Principal Investigator |
KIKUCHI Yasuki Nagasaki University, School of Health Sciences, Associate Professor, 医学部, 助教授 (10124140)
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| Co-Investigator(Kenkyū-buntansha) |
ANRAKU Kazuo Seinan Gakuin University, Department of Literature, Professor, 文学部, 教授 (90184332)
NOMAKUCHI Kentaro Kochi University, Faculty of Science, Professor, 理学部, 教授 (60124806)
MARUYAMA Yukihiro Nagasaki University, Faculty of Economics, Professor, 経済学部, 教授 (30229629)
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| Project Period (FY) |
2000 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥2,900,000 (Direct Cost: ¥2,900,000)
Fiscal Year 2002: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2001: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2000: ¥1,200,000 (Direct Cost: ¥1,200,000)
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| Keywords | NOAEL / information criterion / order restriction / 情報量基準 |
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| Research Abstract |
The summary of research results is as follows.
(1) We review the method to determine the No-Observed-Adverse-Effect Levels ; NOAEL, based on Akaike Information Criterion (AIC, Akaike 1973), which was proposed by Yanagawa et al.(1994, 1997) and Kikuchi et al.(1993) to overcome the criticism of the conventional method using statistical tests.
(2) The above method determines the NOAEL by the idea of model selection using AIC, assuming the order restriction on the parameters in advance. Bias correction term is unknown under the assumption of order restriction, so we consider the estimation of this term using the bootstrap method. It is suggested in this research that the bootstrap method yields a bias under the assumption of order restriction.
(3) Concurrently with this study, we consider EM-algorithm which is used to the estimation based on the incomplete data, both in theoretical and applicative aspects. Theoretically, it is indicated that the generalized EM-Algorithm does not always converges to the maximum likelihood estimate under tha assumption given by Wu (1983).
(4) As an applicative aspect of EM-algorithm, we consider hidden Markov (HM) model. We extend HM model in two ways. One is that incorporated an autoregressive structure and the other has the second order Markov process. The model with the autoregressive structure becomes to fit better than original HM model and the second order model for the wide range of data.
(5) We start to study on the accuracy of the estimates obtained by EM-algorithm when the estimation of the varince based on the observed information matrix is impossible.
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