2021 Fiscal Year Final Research Report
Model Averaging for Ultra-High Dimensional Data: Theory, Methods, and Applications
| Project/Area Number |
19K01582
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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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| Review Section |
Basic Section 07030:Economic statistics-related
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| Research Institution | Otaru University of Commerce |
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Principal Investigator |
LIU QINGFENG 小樽商科大学, 商学部, 教授 (60378958)
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| Project Period (FY) |
2019-04-01 – 2022-03-31
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| Keywords | モデル平均 / モデル選択 / 非線形モデル / 超高次元データ |
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| Outline of Final Research Achievements |
We developed several novel model averaging methods, and provided a solution to the high computational cost issue of model averaging. The achievements include model averaging method of OLS and GLS estimators, model averaging method of nonlinear model, model averaging method for GARCH-type models and model averaging method for Ultra-High Dimensional Data.
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| Free Research Field |
経済統計学
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| Academic Significance and Societal Importance of the Research Achievements |
本研究の成果は、モデル平均法の分野では国際的に最先端の課題のチャレンジで、当該分野の発展に大きく貢献していると言える。ビッグデータの時代において、大規模データに適したデータ解析の方法を提供した。学術と社会の発展に技術の面で寄与した。
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