Doctor of Functional Mathematics

No

Statistical Science, Machine Learning

00years00months

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 Interests

• Research and development of big data analysis methods for the breakthrough of multi-scale structures
  keyword : adhesion, multi-scale analysis
  2018.11～2028.03.
• Forecast of energy consumption
  keyword : Forecast of energy consumption
  2016.04～2018.05.
• Sparse multivariate analysis
  keyword : Sparse estimation, factor anlaysis
  2016.04～2022.03.

Academic Activities Awards

• 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.

Educational Activities

Professional and Outreach Activities