Extensions of longitudinal data analysis
| Project/Area Number |
17K00066
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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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 |
Funatogawa Ikuko 統計数理研究所, データ科学研究系, 准教授 (80407931)
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| Project Period (FY) |
2017-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | 経時データ解析 / 自己回帰 / 線形混合効果モデル / パネルデータ分析 / 統計数学 |
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| Outline of Final Research Achievements |
Longitudinal data are measurements or observations taken from multiple subjects repeatedly over time. We conducted research with the aim of extensions of longitudinal data analysis. In particular, we aimed to integrate and develop methodologies for dynamic models. Dynamic models have evolved separately in several fields. We published the English book "Longitudinal Data Analysis: Autoregressive Linear Mixed Effects Models". We showed the relationship between the proposed autoregressive linear mixed effects models and dynamic models in another field.
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| Academic Significance and Societal Importance of the Research Achievements |
経時データ解析は、様々な分野で用いられ、各分野で発展してきた。特に、ダイナミックモデルは有用であると考えられるが、いくつかの分野で別々に発展してきており、融合による発展の余地が大きい。本研究により、他分野との融合がさらに促進されると期待される。
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Report
(6 results)
Research Products
(15 results)
