| Project/Area Number |
17500180
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | Gifu University |
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Principal Investigator |
SAGAE Masahiko Gifu University, Faculty of Engineering, Associate Professor (20215669)
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| Co-Investigator(Kenkyū-buntansha) |
KOGURE Atsuyuki Keio University, Faculty of Policy Management, Professor (80178251)
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| Project Period (FY) |
2005 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥2,930,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥330,000)
Fiscal Year 2007: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2006: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2005: ¥800,000 (Direct Cost: ¥800,000)
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| Keywords | Nonparametric Inference / Data Squashing / Method of Local Moments / Data Mining / Kernel Method / ノンパラメトリック / カーネル推理 / 確率密度関数 |
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| Research Abstract |
The data squashing is proposed by DuMouchel, et. al. (1999) to deal with massive data sets. The idea is to scale data sets down to smaller representative samples, "squashed data", instead of scaling up algorithms to large data sets. However, the original scheme for the data squashing may still be computationally burdensome because it usually requires solving a large system of equations. In this project, we developed a new method for the data squashing which does not involve solving theoretical properties of the maximum likelihood estimator applied to the squashed data.
Some simulation study was provided to explain some evidence of effectiveness of our new method.
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