| Project/Area Number |
18K12756
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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 | Kwansei Gakuin University |
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Principal Investigator |
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | ベイズ統計学 / マルコフ連鎖モンテカルロ法 / 死因予測 / 条件付き分布 |
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| Outline of Final Research Achievements |
This research develops a new statistical method for prediction of causes of death with verbal autopsy data, taking into account characteristics of surveys about health history and symptoms of the deceased. The proposed statistical model relies on less assumptions than existing methods, by incorporating count, categorical, continuous variables as well as binary ones in the framework. Moreover, for the proposed method, a new algorithm is developed for prediction of causes of death using real survey data.
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| Academic Significance and Societal Importance of the Research Achievements |
提案手法は口頭剖検のための統計手法として，既存のアプローチに比べて聞き取り調査データの特徴を柔軟に捉えることができるため，発展途上国の死因分布の推定精度の向上や既存分析の妥当性の検証のために役立つと考える．加えて，一般の多変量データ解析のための統計手法としても，提案手法は複雑な相関構造や様々な観測尺度を有する高次元データの分析を目的とするため，部分的に他分野への応用も可能である．例えば，多くの社会調査は多数の質問項目から構成され，項目間の複雑な関係性，頻出する欠損値の存在，異なる観測尺度など，本研究で取り組んだデータの特徴との類似点があると考えられる．
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