Journal Papers  (Peer reviewed)

1. Kato, S., Matsui, H., and Terada, M. (2024).
   Revisiting compressive mechanism: differential privacy via compressive sensing using sparse sensing matrices (in Japanese). 
   IPSJ Journal, in press.

2. Matsui, H. and Mochida, K. (2024).
   Functional data analysis-based yield modeling in year-round crop cultivation
   Horticulture Research, in press. (doi: 10.1093/hr/uhae144)

3. Arai, N. Matsui, H., Misumi, T., and Konishi, S. (2024). 
   Multivariate functional data clustering for spatio-temporal data. (in Japanese)
   Japanese Journal of Applied Statistics, in press. 

4. Fukuda, T.*, Matsui, H.*, Takada, H., Misumi, T., and Konishi, S. (2024).  (* Equally contributed)
   Multivariate functional subspace classification for high-dimensional longitudinal data.
   Japanese Journal of Statistics and Data Science 7, 1-16. (doi: 10.1007/s42081-023-00226-x)

5. Watanabe, S. and Matsui, H. (2023).
   Classification from Positive and Biased Negative Data with Skewed Labeled Posterior Probability.
   Neural Computation 35(5), 977-994.  (doi: 10.1162/neco_a_01580)

6. Matsui, H. (2021).  
   Variable selection for historical functional linear model.
   Bulletin of Informatics and Cybernetics 53, 1-19. (doi: 10.5109/4151124 )

7. Inoue, K., Takahagi, K., Kouzai, Y., Koda, S., Shimizu, M., Uehara-Yamaguchi, Y., Nakayama, R., Kita, T., Onda, Y., Nomura, T., Matsui, H., Nagaki, K., Nishii, R., and Mochida, K. (2020).
   Parental Legacy and Regulatory Novelty in Brachypodium Diurnal Transcriptomes Accompanying their Polyploidy.
   NAR Genomics and Bioinformatics 2,  lqaa067. (doi: 10.1093/nargab/lqaa067 )

8. Esaki, T., Horinouchi, T., Natsume-Kitatani, Y., Nojima, Y., and Matsui, H. (2020).   
   Estimation of relationships between gene expression and chemical substructure: adapting canonical correlation analysis for small sample data by gathered features using consensus clustering.
   Chem-Bio Informatics Journal 20, 58-61. (doi: 10.1273/cbij.20.58 )

9. Matsui, H., and Umezu, Y. (2020). 
   Variable selection in multivariate linear models for functional data via sparse regularization.
   Japanese Journal of Statistics and Data Science 3, 453-467. (doi: 10.1007/s42081-019-00055-x)

10. Matsui, H. (2020). 
    Quadratic regression for functional response models. 
    Econometrics and Statistics 13, 125-136.  (doi:10.1016/j.ecosta.2018.12.003)  (R source is available from GitHub)

11. Matsui, H. (2019). 
    Statistical modeling via functional data analysis.  (in Japanese)
    Proceedings of the Institute of Statistical Mathematics 67, 73-96. (PDF)

12. Matsui, H. (2019).  
    Sparse group lasso for multiclass functional logistic regression models.   
    Communications in Statistics - Simulation and Computation 48, 1784-1797. (doi:10.1080/03610918.2018.1423693)

13. Misumi, T., Matsui, H., and Konishi, S. (2019) . 
    Multivariate functional clustering and its application to typhoon data. 
    Behaviormetrika 46, 163-175. (doi:10.1007/s41237-018-0066-8)

14. Kawamoto, K., Ohashi, T., Konno, M., Nishida, N., Koseki, J., Matsui, H., Sakai, D., Kudo, T., Eguchi, H., Satoh, T., Doki, Y., Mori, M., and Ishii, H. (2018).  
    Cell-free culture conditioned medium elicits pancreatic β cell lineage-specific epigenetic reprogramming in mice. 
    Oncology Letters 16, 3255-3259. (doi: 10.3892/ol.2018.9008)

15. Takemura, A.,  Izumi,  S., Saito, M.,  Himeno, T., Matsui, H., and Date, H. (2018).
    Shiga University model of data science education.  (in Japanese)
    Proceedings of the Institute of Statistical Mathematics 66, 63-78.  (PDF)

16. Smaga, Ł., Matsui, H. (2018).   
    A note on variable selection in functional regression via random subspace method. 
    Statistical Methods & Applications 27(3), 455-477. (doi:10.1007/s10260-018-0421-7)

17. Konno, M.*, Matsui, H.*, Koseki, J.*, Asai, A., Kano, Y., Kawamoto, K., Nishida, N., Sakai, D., Kudo, T., Satoh, T., Doki, Y., Mori, M., and Ishii, H. (2018).  (* Equally contributed) 
    Computational trans-omics approach characterised methylomic and transcriptomic involvements and identified novel therapeutic targets for chemoresistance in gastrointestinal cancer stem cells. 
    Scientific Reports 8, Article number: 899. (doi:10.1038/s41598-018-19284-3). 

18. Koda, S.*, Onda, Y.*, Matsui, H.*, Takahagi, K., Yamaguchi-Uehara, Y., Shimizu, M., Inoue, K., Yoshida, T., Sakurai, T., Honda, H., Eguchi, S., Nishii, R., and Mochida, K. (2017).  (* Equally contributed) 
    Diurnal Transcriptome and Gene Network Represented Through Sparse Modeling in Brachypodium distachyon. 
    Frontiers in Plant Science 2017 Nov 28;8 :2055. (doi: 10.3389/fpls.2017.02055). 

19. Miyo, M., Konno, M., Nishida, N., Sueda, T., Noguchi, K., Matsui, H., Colvin, H., Kawamoto, K., Koseki, J., Haraguchi, N., Nishimura, M., Shimizu, H., Doki, Y., Mori, M., and Ishii, H. (2016).
    Metabolic adaptation to nutritional stress in human colorectal cancer.
    Scientific Reports 6, Article number: 38415. (doi:10.1038/srep38415).

20. Matsui, H., Misumi, T., Yokomizo, T., and Konishi. S. (2016).
    Clustering for functional data via nonlinear mixed effects models. (in Japanese)
    Japanese Journal of Applied Statistics 45, 25-45. (doi: 10.5023/jappstat.45.25)

21. Kayano, M.*, Matsui, H.*, Yamaguchi, R., Imoto, S., and Miyano, S. (2016).   (* Equally contributed)
    Gene set differential analysis of time course expression profiles via sparse estimation in functional logistic model with application to time-dependent biomarker detection.  
    Biostatistics 17, 235-248. (doi: 10.1093/biostatistics/kxv037)

22. Koseki, J.*, Matsui, H.*, Konno, M.*, Nishida, N., Kawamoto, K., Kano, Y., Mori, M., Doki, Y., and Ishii, H. (2016). (* Equally contributed)
    A trans-omics mathematical analysis reveals novel functions of the ornithine metabolic pathway in cancer stem cells.
    Scientific Reports 6, Article number: 20726. (doi:10.1038/srep20726)

23. Matsui, H. (2015b).
    Selection of decision boundaries for logistic regression. 
    Bulletin of Informatics and Cybernetics 47, 83-95.  (doi: 10.5109/1909526)

24. Matsui, H. (2015a).  
    Sparse regularization for bi-level variable selection.  
    Journal of the Japanese Society of Computational Statistics 28, 83-103. (doi: 10.5183/jjscs.1502001_216)

25. Konno, M., Koseki, J., Kawamoto, K., Nishida, N., Matsui, H., Dewi, D. L., Ozaki, M., Noguchi, Y., Mimori, K. Gotoh, N., Doki, Y., Mori, M., and Ishii, H. (2015).
    Embryonic microRNA-369 controls metabolic splicing factors and urges cellular reprogramming.
    PLoS ONE 10(7): e0132789. (doi: 10.1371/journal.pone.0132789)

26. Ogawa, H., Wu, X., Kawamoto, K., Nishida, N., Konno, M., Koseki, J., Matsui, H., Noguchi, K., Gotoh, N., Yamamoto, T., Miyata, K., Nishiyama, N., Nagano, H., Yamamoto, H., Obika, S., Kataoka, K., Doki, Y., Mori, M., and Ishii, H. (2015).
    MicroRNAs induce epigenetic reprogramming and suppress malignant phenotypes of human colon cancer cells.
    PLoS ONE 10(5): e0127119. (doi:10.1371/journal.pone.0127119)

27. Konno, M., Ishii, H., Koseki, J., Tanuma, N., Nishida, N., Kawamoto, K., Nishimura, T., Nakata, A., Matsui, H., Noguchi, K., Ozaki, M., Noguchi, Y., Shima, H., Gotoh N., Nagano, H., Doki, Y., and Mori, M. (2015).  
    Pyruvate kinase M2, but not M1, allele maintains immature metabolic states of murine embryonic stem cells.  
    Regenerative Therapy 1, 63-71.  (doi:10.1016/j.reth.2015.01.001)

28. Matsui, H. and Misumi, T. (2015).  
    Variable selection for varying coefficient models with the sparse regularization.  
    Computational Statistics 30(1), 43-55.  (doi:10.1007/s00180-014-0520-3)

29. Koseki, J., Colvin, H., Fukusumi, T., Nishida, N., Konno, M., Kawamoto, K., Tsunekuni, K., Matsui, H., Doki, Y., Mori, M., and Ishii, H. (2015).  
    Mathematical analysis predicts imbalanced IDH1/2 expression associates with 2-HG-inactivating β-oxygenation pathway in colorectal cancer.  
    International Journal of Oncology 46(3), 1181-1191.  (doi:10.3892/ijo.2015.2833)

30. Matsui, H. (2014).  
    Variable and boundary selection for functional data via multiclass logistic regression modeling.  
    Computational Statistics & Data Analysis 78, 176-185.  (doi:10.1016/j.csda.2014.04.015)

31. Matsui, H., Misumi, T., and Kawano, S. (2014).  
    Model selection criteria for the varying-coefficient modeling via regularized basis expansions.  
    Journal of Statistical Computation and Simulation 84(10), 2156-2165.  (doi:10.1080/00949655.2013.785548)

32. Matsui, H. and Konishi, S. (2011).  
    Variable selection for functional regression model via the L1 regularization.  
    Computational Statistics & Data Analysis 55(12), 3304-3310.  (doi:10.1016/j.csda.2011.06.016)

33. Matsui, H., Araki, T., and Konishi, S. (2011).  
    Multiclass functional discriminant analysis and its application to gesture recognition.  
    Journal of Classification 28(2), 227-243.  (doi:10.1007/s00357-011-9082-z)

34. Tateishi, S., Matsui, H., and Konishi, S. (2010).  
    Nonlinear regression modeling via the lasso-type regularization.  
    Journal of Statistical Planning and Inference 140, 1125-1134.  (doi:10.1016/j.jspi.2009.10.015)

35. Araki, Y., Konishi, S., Kawano, S., and Matsui, H. (2009).  
    Functional logistic discrimination via regularized basis expansions.
    Communications in Statistics - Theory and Methods 38, 2944-2957. (doi:10.1080/03610920902947246)

36. Araki, Y., Konishi, S., Kawano, S., and Matsui, H. (2009).  
    Functional regression modeling via regularized Gaussian basis expansions. 
    Annals of the Institute of Statistical Mathematics 61, 811-833. (doi:10.1007/s10463-007-0161-1)

37. Matsui, H., Kawano, S., and Konishi, S. (2009).  
    Regularized functional regression modeling for functional response and predictors.  
    Journal of Math-for-Industry 1, 17-25.  (PDF)

38. Matsui, H., Araki, Y., and Konishi, S. (2008).  
    Multivariate regression modeling for functional data.  
    Journal of Data Science 6, 313-331. (PDF)

Books

1. 松井秀俊（著）(2023)．
   多変量解析（データサイエンス大系）
   学術図書出版社，ISBN: 978-4-7806-0707-9 (web)　サポートページはこちら (web)

2. Kroese, D. P., Botev, Z. I., Taimre, T., & Vaisman, R.  (著)，金森敬文 (監訳) (2022)．
   データサイエンスと機械学習  理論からPythonによる実装まで．
   東京化学同人，ISBN: 978-4-8079-2029-7 (web)

3. 笛田薫，松井秀俊（著）(2022)．
   Excelで学べるデータサイエンス入門講義．
   日経BP，ISBN: 978-4296200146 (web)

4. 松井秀俊，小泉和之 (著)，竹村彰通  (編) (2019). 
   統計モデルと推測（データサイエンス入門シリーズ）．
   講談社サイエンティフィク，ISBN: 978-4-06-517802-7 (web)      正誤表はこちら： PDF 

5. 滋賀大学データサイエンス学部（編）(2019).
   大学生のためのデータサイエンス(II)ーオフィシャルスタディノート．
   日本統計協会，ISBN: 978-4-8223-4053-7 (web)

6. 竹村彰通，姫野哲人，高田聖治 (編)，和泉志津恵，市川治，梅津高朗，北廣和雄，齋藤邦彦，佐藤智和，白井剛，高田聖治，竹村彰通，田中琢真，姫野哲人，松井秀俊 (著)  (2019)．
   データサイエンス入門（データサイエンス大系）．
   学術図書出版社，ISBN: 978-4-7806-0701-7 (web)
   第2版：2021年4月出版　ISBN: 978-4-7806-0730-7 (web)

7. Miodrag Lovric (編), 日本統計学会（翻訳）(2018)．
   統計科学百科事典．
   丸善出版，ISBN: 978-4-621-30310-8 (web)

8. 滋賀大学データサイエンス学部（編）(2018)．
   大学生のためのデータサイエンス(I)ーオフィシャルスタディノート．
   日本統計協会，ISBN: 978-4-8223-4009-4 (web)

9. 川野秀一，松井秀俊，廣瀬慧 (2018)．
   スパース推定法による統計モデリング（統計学One Point）．
   共立出版，ISBN: 978-4320112575 (web)

10. Peter Flach (著), 竹村 彰通 (監修, 翻訳), 田中 研太郎, 小林 景, 兵頭 昌, 片山 翔太, 山本 倫生, 吉田 拓真, 林 賢一, 松井 秀俊, 小泉 和之, 永井 勇 (翻訳) (2017)．
    機械学習―データを読み解くアルゴリズムの技法．
    朝倉書店，ISBN: 978-4254122183 (web)

Book Reviews

1. 書評：Statistics for High-Dimensional Data: Methods, Theory and Applications (Buhlmann, P. and van de Geer, S. 著, Springer, 2011).   日本統計学会誌 44(1), 247-249.  

Dissertation

• • 関数データに基づく多変量回帰モデリング (2006)． 修士論文，九州大学大学院数理学府．

  • Regularized Functional Regression Modeling and its Applications [邦題：正則化法に基づく関数回帰モデリングとその応用] (2009)． 博士論文，九州大学大学院数理学府．