| 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)
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| 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.
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