|Hirose Kei||Last modified date：2022.04.06|
Associate Professor / Institute of Mathematics for Industry
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Reseacher Profiling Tool Kyushu University Pure
Doctor of Functional Mathematics
Country of degree conferring institution (Overseas)
Field of Specialization
Statistical Science, Machine Learning
Total Priod of education and research career in the foreign country
We are developing practical methods for multivariate analysis that takes the covariance structures into consideration, such as factor analysis, structural equation modeling, graphical models, and canonical discriminant analysis. Specifically, we propose a new method based on structural regularization and clustering based on prediction, develop several numerical algorithms that efficiently compute the estimate of the parameter, and investigate theoretical properties of the estimated parameters. Most of the proposed methods are available for use in the R packages. Also, I have been working on applied statistics, such as electricity demand forecasting and material properties prediction.
- Research and development of big data analysis methods for the breakthrough of multi-scale structures
keyword : adhesion, multi-scale analysis
- Forecast of energy consumption
keyword : Forecast of energy consumption
- Sparse multivariate analysis
keyword : Sparse estimation, factor anlaysis
- The following paper
「Kei Hirose， Yukihiro Ogura and Hidetoshi Shimodaira. Estimating scale-free networks via the exponentiation of minimax concave penalty. Journal of the Japanese Society of Computational Statistics. 28 (1), pp.139-154, 2015」
and several other papers related to sparse multivariate anlaysis.
I am in charge of School of Interdisciplinary Science and Innovation, Department of Mathematics, Graduate School of Mathematics, and Graduate School of Systems Life Sciences. Educational activities are conducted through lectures and seminars.