Doctor of Functional Mathematics

Statistical Science, Machine Learning

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. Recently, I have been working on the forecast of electricity demand.

Research Interests

• Forecast of energy consumption
  keyword : Forecast of energy consumption
  2016.04～2018.05.
• Development of sparse factor analysis
  keyword : Sparse estimation, factor anlaysis
  2016.04～2017.03.

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