2020 Fiscal Year Final Research Report
Objective Bayes methods for non-regular statistical models
| Project/Area Number |
17K14233
|
|---|
| Research Category |
Grant-in-Aid for Young Scientists (B)
|
| Allocation Type | Multi-year Fund |
|---|
| Research Field |
Foundations of mathematics/Applied mathematics
|
|---|
| Research Institution | Hiroshima University |
|---|
Principal Investigator |
|
|---|
| Project Period (FY) |
2017-04-01 – 2021-03-31
|
| Keywords | ベイズ統計学 / 統計数学 / 客観事前分布 / 非正則モデル / 高次漸近理論 |
|---|
| Outline of Final Research Achievements |
I studied the selection of objective priors in Bayesian statistics. In particular, I derived objective priors and studied these properties for non-regular models by using three approaches. As a related study, we studied the theory and methodology for robust Bayesian inference with my collaborators.
|
| Free Research Field |
数理統計学
|
| Academic Significance and Societal Importance of the Research Achievements |
ベイズ統計学においては事前分布の選択が重要であり，理論的根拠を備えた事前分布の選択法はベイズ統計学の客観的な利用において必須である．特に，理想的な条件が成り立っていない非正則な状況は実際問題では自然であるが従来の統計理論が使えないため，事前分布の観点から新たな知見を与えたことは意義あることである．
|
