Centers & Labs

RIKEN Center for Advanced Intelligence Project

Mathematical Statistics Team

Team Leader: Hidetoshi Shimodaira (D.Eng.)
Hidetoshi  Shimodaira(D.Eng.)

We pursue the methodology of statistics and machine learning. Statistics has been playing important roles as a theoretical basis for data science and artificial intelligence. It provides the methodology of inductive inference by considering probability. We believe that working on real data analysis will lead to the development of theory and methods of statistics. We developed a method of statistical hypothesis testing (multiscale bootstrap) which is now commonly used for DNA sequence analysis and gene expression analysis. We also developed a theory of information criterion for the transfer learning (covariate shift) of machine learning. Recently, we are also working on statistical inference of the growth mechanism of complex networks, and multivariate analysis methods and their deep learning for integrating several types of data such as images and sentences.

Main Research Field

Computer Science

Related Research Fields

Molecular Biology & Genetics / Mathematics

Research Subjects

  • Statistics
  • Machine Learning

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.
  1. *Shimodaira, H.:
    “Cross-validation of matching correlation analysis by resampling matching weights”
    Neural Networks 75, 126-140 (2016).
  2. *Pham, T., Sheridan, P., and Shimodaira, H.:
    “PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks”
    PLoS ONE 10, e0137796 (2015).
  3. *Shimodaira, H.:
    “Higher-order accuracy of multiscale-double bootstrap for testing regions”
    Journal of Multivariate Analysis 130, 208-223 (2014).
  4. *Shimodaira, H.:
    “Testing regions with nonsmooth boundaries via multiscale bootstrap”
    Journal of Statistical Planning and Inference 138, 1227-1241 (2008).
  5. *Suzuki, R. and Shimodaira, H.:
    “pvclust: an R package for assessing the uncertainty in hierarchical clustering”
    Bioinformatics 22, 1540-1542 (2006).
  6. *Shimodaira, H.:
    “Approximately unbiased tests of regions using multistep-multiscale bootstrap resampling” Annals of Statistics 32, 2616-2641 (2004).
  7. *Shimodaira, H.:
    “An approximately unbiased test of phylogenetic tree selection”
    Systematic Biology 51, 492-508 (2002).
  8. *Shimodaira, H. and Hasegawa, M.:
    “CONSEL: for assessing the confidence of phylogenetic tree selection”
    Bioinformatics 17, 1246-1247 (2001).
  9. *Shimodaira, H.:
    “Improving predictive inference under covariate shift by weighting the log-likelihood function”
    Journal of Statistical Planning and Inference 90, 227-244 (2000).
  10. *Shimodaira, H. and Hasegawa, M.:
    “Multiple comparisons of log-likelihoods with applications to phylogenetic inference” Molecular Biology and Evolution 16, 1114-1116 (1999).

Contact information

Graduate School of Informatics, Kyoto University,
Yoshida Honmachi,
Sakyo-ku, Kyoto,
606-8501, Japan

Email: shimo [at] i.kyoto-u.ac.jp

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