2013 Fiscal Year Final Research Report
Nonlinear modeling based on high-dimensional data
| Project/Area Number |
21300106
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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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 | Chuo University (2010-2013)
Kyushu University (2009) |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
NISHI Ryuei 九州大学, MI 研究所, 教授 (40127684)
MAESONO Yoshihiko 九州大学, 大学院・数理学研究院, 教授 (30173701)
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| Co-Investigator(Renkei-kenkyūsha) |
NINOMIYA Yoshiyuki 九州大学, MI 研究所, 准教授 (50343330)
MASUDA Hiroki 九州大学, MI 研究所, 准教授 (10380669)
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| Project Period (FY) |
2009-04-01 – 2014-03-31
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| Keywords | 非線形モデリング / スパースモデリング / 高次モデル評価基準 / 正則化法 / ベイズモデリング |
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| Research Abstract |
The development of electronic measurement and instrumentation technologies enables us to accumulate a huge amount of data with complex structure and/or high-dimensional data. Through this research project we have investigated the problem of analyzing such datasets, and proposed various statistical modeling strategies including nonlinear regression modeling via L1 regularization, model evaluation and selection criteria, and Bayesian statistical modeling. Various regularization methods with L1 norm penalty have been investigated both in theoretical and numerical aspects. The problem of evaluating statistical models is a crucial issue in model building process. We proposed various types of model evaluation and selection criteria from both an information-theoretic point of view and a Bayesian approach. The proposed techniques may be used in the extraction of useful information and patterns from data with complex structure in the life sciences, systems engineering, and other fields.
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