Doctor of Functional Mathematics

Statistical Science, Machine Learning

To analyze large-scale data such as gene expression data, we often need a statistical model which consists of a large number of parameters (e.g., hundreds of millions.) The sparse estimation, such as the lasso, makes most of the parameters exactly zero, enabling an efficient extraction of useful information from the data. Recently, I am interested in the development of new sparse estimation procedures in multivariate analysis, such as the factor analysis and the Gaussian graphical modeling. Specifically, I am developing several numerical algorithms that efficiently compute the estimate of the parameter, and also investigating theoretical properties of the estimated parameters. Most of the proposed methods are available for use in the R packages.

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.