| Project/Area Number |
14580354
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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 Institute of Statistical Mathematics |
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Principal Investigator |
NAKAMURA Takashi The Institute of Statistical Mathematics, Department of Statistical Methodology, Professor, 調査実験解析研究系, 教授 (20132699)
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| Co-Investigator(Kenkyū-buntansha) |
MAEDA Tadahiko The Institute of Statistical Mathematics, Department of Statistical Methodology, Associate Professor, 調査実験解析研究系, 助教授 (10247257)
OHNO Yuko Graduate School of Osaka University, Department of Mathematical Health Science, Professor, 大学院・医学系研究科, 教授 (60183026)
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| Project Period (FY) |
2002 – 2003
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| Project Status |
Completed (Fiscal Year 2003)
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| Budget Amount *help |
¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2003: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2002: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | age effects / period effects / cohort effects / identification problem / Bayesian model / age-by-period data / 医学関連データ / 2相モデル |
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| Research Abstract |
Cohort analysis is a method of separating the effects of age, historical period, and birth time (cohort) from repeated social survey data classified by age group and survey period. Although the method is useful for understanding the mechanism of social change, it is known that the method confronts the identification problem that the three kinds of effects cannot be separated without some prior information. In order to overcome the problem, one of the authors, Nakamura, proposed a Bayesian cohort model with the gradually -clanging -parameter assumption and a model selection scheme using Akaike's Bayesian information criterion (ABIC).
The purpose of the present research is to develop a new Bayesian cohort model allowing two or more phases of changing structure of age, period, and cohort effects.
The design matrix of the proposed model is set up and ABIC is derived. By analyzing artificial datasets, the performance of the model is examined. Actual datasets such as rates of sports and recreational activities, proportions of dental caries, incidence rates of cancers, data of the Japanese national character study are analyzed using the proposed model.
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