Improvement of statistical inference based on nonparametric smoothing statistics
| Project/Area Number |
15K11995
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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 |
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2016: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2015: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
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| Keywords | ノンパラメトリック / ハザード関数 / 順位検定 / カーネル型統計量 / 平均二乗誤差 / 漸近理論 / 境界問題 / 密度関数比 / カーネル分布関数推定 / 生存時間解析 / 密度比 / ウィルコクソン検定 |
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| Outline of Final Research Achievements |
In this project, we succeed to construct direct kernel estimators of ratio functions such as a density ratio, a hazard ratio and a conditional density. These estimators are sort of transformation estimators that reduce variances. We also succeed to solve problems of discreteness of rank test statistics. Furthermore, we propose new kernel type estimators which eliminate boundary biases and apply them to a mean residual life function.
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Report
(4 results)
Research Products
(27 results)
