An extension to multi-fused criterion for Renyi divergence and its application to apportionment
| Project/Area Number |
18K03413
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Review Section |
Basic Section 12040:Applied mathematics and statistics-related
|
|---|
| Research Institution | Osaka University |
|---|
Principal Investigator |
Hamada Etsuo 大阪大学, 基礎工学研究科, 特任教授(常勤) (20273617)
|
|---|
| Project Period (FY) |
2018-04-01 – 2021-03-31
|
| Project Status |
Completed (Fiscal Year 2020)
|
| Budget Amount *help |
¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2020: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2019: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
|
|---|
| Keywords | ダイバージェンス / モデル評価基準 / ロバストネス / 多層融合化 / ロバスト / 因果性 / 指数型分布族 / モデル評価規準 / レニー・ダイバージェンス / 多層融合化規準 / 議員定数問題 |
|---|
| Outline of Final Research Achievements |
In response to this research, we derived a divergence family of criteria that generalizes AIC by a robust generalization of the model selection criterion family, showed that this family is asymptotically equivalent to AIC in polynomial regression models, and further showed that it performs well even in the presence of outliers. Based on these results, we proposed a new information criterion, RCC, which is based on the influence function, as a robust information criterion for analyzing data with outliers using polynomial regression models, and which has both the consistency corresponding to BIC and the robustness corresponding to BHHJ.
|
| Academic Significance and Societal Importance of the Research Achievements |
モデル選択基準族において、外れ値を持つデータに対しても十分有効なパフォーマンスを発揮する情報量規準の提案が望まれていたが、この研究成果では多項式回帰モデルという制限下ではあるが、一致性と頑健性を併せ持つ新しい情報量規準 RCC を提案することが出来たのは、大きな学術的意義を持つと同時に、データサイエンスの現場での応用可能という意味で社会的意義を持つといえる。
|
Report
(4 results)
Research Products
(4 results)
