Improvement of nonparametric inference which has smoothness and higher order efficiency
| Project/Area Number |
24650151
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Statistical science
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| Research Institution | Kyushu University |
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Principal Investigator |
MAESONO Yoshihiko 九州大学, 数理(科)学研究科(研究院), 教授 (30173701)
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| Project Period (FY) |
2012-04-01 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2013: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2012: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | ノンパラメトリック / 順位検定 / カーネル型推定量 / 符号検定 / 正規近似 / 高次漸近理論 / ウィルコクソン検定 / エッジワース展開 / ノンパラメトリック推測 / ハザード関数 / カーネル法 / ノンパラメトリック検定 / 漸近理論 |
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| Outline of Final Research Achievements |
In this project, we propose smoothed rank tests based on the kernel method which gives us smooth statistical inference. The proposed tests conquer the problem of the discreteness of the distribution for rank tests. We also obtain theoretical properties of the smoothed rank tests, and show the proposed tests are asymptotically equivalent to the ordinary rank tests. Further we obtain Edgeworth expansions of these tests, which are improvements of the normal approximations. If we choose proper kernels, we can get the Edgewroth expansions which do not depend on the population distribution. These results are unique and forefront of this area.
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Report
(4 results)
Research Products
(22 results)
