Statistics for Big Data: Development of Theories and Tackling the 3Vs
Project/Area Number |
26540010
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Research Category |
Grant-in-Aid for Challenging Exploratory Research
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Allocation Type | Multi-year Fund |
Research Field |
Statistical science
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Research Institution | University of Tsukuba |
Principal Investigator |
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Co-Investigator(Kenkyū-buntansha) |
矢田 和善 筑波大学, 数理物質系, 准教授 (90585803)
赤平 昌文 筑波大学, 数理物質系(名誉教授), 名誉教授 (70017424)
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Project Period (FY) |
2014-04-01 – 2017-03-31
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Project Status |
Completed (Fiscal Year 2016)
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Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2015: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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Keywords | ビッグデータ / 潜在構造分析 / 異常値 / 欠損値 / 非正則推定論 |
Outline of Final Research Achievements |
In this research project, we aim to pioneer new statistical theories for big data, ahead of the world. We have developed new theories and methodologies in latent structural analysis for big data: irregular and non-Gaussian data contaminated with outliers and missing values. New theories and methodologies guarantee stable and high accuracy at low computational cost. The findings of this research project are as follows: (1) Developments of the irregular inference theory for big data with diversity. (2) Developments of high-speed and highly accurate latent structural analysis for big data. (3) Pioneering latent structural analysis robust against outliers and missing values.
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Report
(4 results)
Research Products
(34 results)
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[Presentation] 高次元データの統計数理2015
Author(s)
青嶋 誠
Organizer
筑波大学数理物質融合科学センター数理連携サロン
Place of Presentation
筑波大学(茨城県つくば市)
Year and Date
2015-12-02
Related Report
Invited
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