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RIKEN Center for Advanced Intelligence Project Business and Economic Information Fusion Analysis Team

Team Leader: Takahiro Hoshino (Ph.D.)

Research Summary

Takahiro  Hoshino(Ph.D.)

In the era of rapid technological innovation and high social and economic uncertainty, government and companies are required to make decisions more quickly than ever. Although various large big-data such as transaction logs and location information, various studies showed that they are not useful for managerial or policy decision making as it is, because the big-data suffer from various biases such as selection bias. This team will develop new data-fusion techniques for various types of datasets including governmental survey data, big-data and macro-level information, to improve accuracy of public statistical information, or to aid investment/managerial decision making. We also investigate new data acquisition methods in business and economic fields which utilize statistical machine learning methods.

Main Research Fields

  • Economics & Business

Related Research Fields

  • Neuroscience & Behavior
  • Computer Science
  • Mathematics
  • Social Sciences & General

Research Subjects

  • Development of Data fusion techniques
  • Development of new data acquisition methods in business and economic fields
  • Inference with anonymization of big-data in business and economics fields

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.

  • 1.Takahata K., and Hoshino, T.:
    "Identification of heterogeneous treatment effects as a function of potential untreated outcome under the nonignorable assignment condition."
    Keio-IES Discussion Paper Series. (2018)
  • 2.Igari, R., and Hoshino, T.:
    "Bayesian Data Combination Approach for Repeated Durations under Unobserved Missing Indicators"
    Computational Statistics & Data Analysis, 126, 150-166. (2018)
  • 3.*Okada,K. and Hoshino, T.:
    "Researchers’ Choice of Number and Range of Levels in Experiments Affects the Resultant Variance-Accounted-For Effect Size"
    Psychonomic Bulletin & Review, 24(2), 607-616 (2017).
  • 4.*Hoshino, T.:
    "Semiparametric Bayesian Estimation for Marginal Parametric Potential Outcome Modeling: Application to Causal Inference"
    Journal of the American Statistical Association, 108, 1189-1204. (2013)
  • 5.*Hoshino, T. and Bentler, P.M.:
    "Bias in Factor Score Regression and a Simple Solution"
    In Analysis of Mixed Data : Methods & Applications (A.R. de Leon & K. C. C. Carriere,eds). (2013)
  • 6.*Hoshino, T.
    "Bayesian Significance Testing and Multiple Comparisons from MCMC Outputs"
    Computational Statistics & Data Analysis, 52, 3543-3559. (2008)
  • 7.*Kurata, H., Hoshino, T. and Fujikoshi, Y.
    "Allometric Extension Model for Conditional Distributions".
    Journal of Multivariate Analysis, 99 1985-1998. (2008)
  • 8.*Hoshino, T.
    "A Bayesian Propensity Score Adjustment for Latent Variable Modeling and MCMC algorithm"
    Computational Statistics & Data Analysis, 52, 1413-1429. (2008)
  • 9.*Hoshino, T.
    "Doubly Robust type Estimation for Covariate Adjustment in Latent Variable Modeling"
    Psychometrika, 72 535-549. (2007)

Related Links

Lab Members

Principal investigator

Takahiro Hoshino
Team Leader

Contact Information

2-15-45, Mita,
Minato-ku, Tokyo, 108-8345
Email: bayesian [at] jasmine.ocn.ne.jp