Inference via functional theory and L1 regularization modeling
| Project/Area Number |
25610035
|
|---|
| Research Category |
Grant-in-Aid for Challenging Exploratory Research
|
| Allocation Type | Multi-year Fund |
|---|
| Research Field |
Foundations of mathematics/Applied mathematics
|
|---|
| Research Institution | Chuo University |
|---|
Principal Investigator |
|
|---|
| Project Period (FY) |
2013-04-01 – 2016-03-31
|
| Project Status |
Completed (Fiscal Year 2015)
|
| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2013: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
|
|---|
| Keywords | スパースモデリング / ベイズモデリング / 非線形モデリング / 混合効果モデリング / モデル評価基準 / 汎関数理論 / 関数混合効果モデリング / L1 ノルム正則化法 / 次元圧縮 / 高次モデル評価基準 / カーネル識別・判別法 |
|---|
| Outline of Final Research Achievements |
In various fields of science and industry a huge amount of data with complex structure and/or high-dimensional data have been accumulating. The effective use of these data sets requires new modeling strategies in order to perform extraction of useful information and knowledge discovery. Through this research we have investigated the problem of analyzing such datasets, and proposed various statistical modeling strategies: (1) Various regularization methods with L1 norm penalty have been proposed for effective regression modeling from a Bayesian point of view. (2) For analyzing data with complicated structure or substantial longitudinal heterogeneity between subjects, we introduced a varying coefficient modeling and a nonlinear functional mixed modeling through the nonlinear regression approach. (3) We developed a general framework for constructing model selection criteria in the context of functional statistics.
|
Report
(4 results)
Research Products
(16 results)
