| Project/Area Number |
18K12758
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 07030:Economic statistics-related
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| Research Institution | Kyushu University (2019-2022)
The Institute of Statistical Mathematics (2018) |
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Principal Investigator |
Hirose Masayo 九州大学, マス・フォア・インダストリ研究所, 助教 (30739199)
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2021: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
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| Keywords | 統計的推測 / 貧困率推定 / 小地域推定 / 統計的開示制御 / 統計的推測法 / 経験的ベイズ法 / 平均二乗予測誤差 / 統計 / 公的ミクロデータ / 貧困率推定法 / 貧困対策 / 格差是正 |
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| Outline of Final Research Achievements |
To understand the poverty situation in each small area, I have addressed developing a statistical estimation method from theoretical and practical views. And I have also tried to consider whether it is applicable to real Japanese data.
As a result, we have obtained several research results. Noteworthy, one resulting paper has been published in top international journals in statistical science. Moreover, I also presented several results at domestic and international conferences and meetings. Furthermore, under a specific statistical model, we have also worked on developing statistical methods in small area estimation, considering prediction error. Thereby I believe that these results make the applicability of our method to poverty rate estimation.
However, some issues still have not been addressed at this time. Therefore, I will continue to study in the near future.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究の成果により, 想定しているデータ発生モデルに妥当性がある場合, より効率的な小行政区分別貧困率の推定の実現が期待できる. その結果, 実態を反映しやすい貧困実態把握資料作成が可能となり, より効率的な貧困対策や格差是正対策への期待を高められると考えている. さらに, ある仮定の下であれば, パンデミック初期段階であっても従来法より信頼性の高い分析に貢献するのではないかと期待している. すなわち, 医学・医療データ分析にも将来活用可能である. もちろん, さらなる応用分野にも活用されうると考えている.
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