Shintaro Hashimoto's website

I am a member of ISBA (International Society for Bayesian Analysis), MJS (Mathematical Society of Japan) and JSS (Japan Statsitical Society).

Research Interests

• Bayesian inference
• Higher order asymptotic theory
• Information inequalities
• Monte Carlo methods
• Non-regular statistical models

Education

• 2015 Ph.D. Department of Mathematics, University of Tsukuba, Japan
• 2013 M.A. Department of Mathematics, University of Tsukuba, Japan
• 2011 B.A. Department of Mathematics, University of Tsukuba, Japan

Preprints

1. Nishina, S., Onizuka, T. and Hashimoto, S. (2026). Global-local shrinkage priors for modeling random effects in multivariate spatial small area estimation. Submitted. R-code, arXiv.
2. Iwashige, F., Wakayama, T., Sugasawa, S. and Hashimoto, S. (2025). On misspecified error distributions in Bayesian functional clustering: Consequences and remedies, arXiv.
3. Iwashige, F. and Hashimoto, S. (2025). Bayesian mixture modeling using a mixture of finite mixtures with normalized inverse Gaussian weights. Submitted. R-code, arXiv.
4. Hamura, Y., Onizuka, T., Hashimoto, S. and Sugasawa, S. (2025). Robust Bayesian inference for censored survival models. Submitted. R-code, arXiv.

Publications

1. Onizuka, T. and Hashimoto, S. (2025). Robust Bayesian graphical modeling using γ-divergence. Journal of Multivariate Analysis. 209, 105461, R-code, arXiv, web.
2. Hamura, Y., Onizuka, T., Hashimoto, S. and Sugasawa, S. (2024). Sparse Bayesian inference on gamma-distributed observations using shape-scale inverse-gamma mixtures. Bayesian Analysis, 19(1), 77-97. R-code, arXiv, web.
3. Onizuka, T., Hashimoto, S. and Sugasawa, S. (2024). Locally adaptive spatial quantile smoothing: Application to monitoring crime density in Tokyo. Spatial Statistics, 59, 100793. R-code, arXiv, web.
4. Onizuka, T., Iwashige, F. and Hashimoto, S. (2024). Bayesian boundary trend filtering. Computational Statistics and Data Analysis, 191, 107889. R-code, arXiv, web.
5. Onizuka, T., Hashimoto, S. and Sugasawa, S. (2024). Fast and locally adaptive Bayesian quantile smoothing using calibrated variational approximations. Statistics and Computing, 34, article number: 15. R-code, arXiv, web.
6. Kawakami, J. and Hashimoto, S. (2023). Approximate Gibbs sampler for Bayesian Huberized lasso. Journal of Statistical Computation and Simulation, 93(1), 128-162. arXiv, web.
7. Hashimoto, S. (2021). Predictive probability matching priors for a certain non-regular model. Statistics and Probability Letters, 174, 109096. web.
8. Hashimoto, S. (2021). Reference priors via α-divergence for a certain non-regular model in the presence of a nuisance parameter. Journal of Statistical Planning and Inference, 213, 162-178. web, Technical Report.
9. Sugasawa, S. and Hashimoto, S. (2021). Robust Bayesian changepoint analysis in the presence of outliers. Proceedings of the 13th KES-IDT 2021 Conference (eds. Czarnowski, I., Howlett, R. J. & Jain, L. C.), Smart Innovation, Systems and Technologies, 469-478. web.
10. Koike, K. and Hashimoto, S. (2021). Improvement of Bobrovsky-Mayor-Wolf-Zakai bound. Entropy, 23(2), 161. web
11. Nakagawa, T. and Hashimoto, S. (2021). On default priors for robust Bayesian estimation with divergences. Entropy, 23(1), 29. arXiv, web.
12. Hashimoto, S. and Sugasawa, S. (2020). Robust Bayesian regression with synthetic posterior distributions. Entropy, 22(6), 661. R-code, arXiv, web.
13. Nakagawa, T. and Hashimoto, S. (2020). Robust Bayesian inference via γ-divergence, Communications in Statistics - Theory and Methods, 49(2), 343-360. web.
14. Hashimoto, S. (2019). Moment matching priors for non-regular models, Journal of Statistical Planning and Inference, 203, 169-177. web, Technical Report.
15. Hashimoto, S. (2017). Robust estimation for skew-normal distribution with location and scale parameters via log-regularly varying functions. International Journal of Statistics and Systems, 12(4), 813-822. web.
16. Akahira, M., Hashimoto, S., Koike, K. and Ohyauchi, N. (2016). Second order asymptotic comparison of the MLE and MCLE for a two-sided truncated exponential family of distributions. Communications in Statistics - Theory and Methods, 45(19), 5637-5659. web.
17. Hashimoto, S. and Koike, K. (2015). Bhattacharyya type information inequality for the Bayes risk. Communications in Statistics - Theory and Methods, 44(24), 5213-5524. web.

Grants

• Grant-in-Aid for Scientific Research (C), Grant Number: 25K07131, 2025-2029 (Principal Investigator)
• Grant-in-Aid for Scientific Research (A), Grant Number: 25H00546, 2025-2030 (Co-Investigator)
• Grant-in-Aid for Early-Career Scientists, Grant Number: 21K13835, 2021-2024 (Principal Investigator)
• Grant-in-Aid for Scientific Research (B), Grant Number: 21H00699, 2021-2024 (Co-Investigator)
• Grant-in-Aid for Scientific Research (C), Grant Number: 20K11702, 2020-2022 (Co-Investigator)
• Grant-in-Aid for Young Scientists (B), Grant Number: 17K14233, 2017-2020 (Principal Investigator)
• University of Tsukuba Basic Research Support Problem Type A, 360,000 yen, 2014-2015

Ph.D Students

• Takahiro Onizuka (2022-2024): He is currently working as a Lecturer at Graduate School of Social Sciences, Chiba University
• Fumiya Iwashige (2024-)

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教育・研究・科研費 (in Japanese)

• 講義・学生指導のページへ (担当講義と学生指導記録)
• 広島大学研究者総覧
• researchmap
• 科研費

書籍

• 標準ベイズ統計学，朝倉書店，ピーター・D・ホフ (著)，入江薫・菅澤翔之助・橋本真太郎 (訳), 2022年
  Japanese translation of "A First Course in Bayesian Statistical Methods" by Peter, D. Hoff (Springer)
  
  正誤表（第3刷以降）
  

エッセイ・研究紹介など (in Japanese)

• スタンフォード滞在記，(シリーズ「研究滞在記」)，日本統計学会会報, No. 170, pp. 7-9.
• 研究紹介（研究セレクション・広島大学大学院先進理工系科学研究科ホームページ）
• 夢ナビ講義動画「不確実性を評価する」（高校生向け）
• 広島大学総合科学部報「飛翔」（99号, p12-14, インタビュー記事）