Publications 出版物
Books
滋賀大学，PwC Japan有限責任監査法人（編）(2024).
「ＡＩ監査」の基本と技術―データサイエンティストの活躍
中央経済社，ISBN: 978-4-502-44711-2 (web)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)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)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)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)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)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)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)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)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)
Journal Papers (Peer reviewed)
新井仁智，松井秀俊，三角俊裕，小西貞則 (2024).
空間経時測定データに対する多変量関数クラスタリング (English Title: Multivariate functional data clustering for spatio-temporal data).
応用統計学，印刷中．Fukuda, T.*, Matsui, H.*, Takada, H., Misumi, T., and Konishi, S. (2023). (* Equally contributed)
Multivariate functional subspace classification for high-dimensional longitudinal data.
Japanese Journal of Statistics and Data Science, in press (doi: 10.1007/s42081-023-00226-x)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)Matsui, H. (2021).
Variable selection for historical functional linear model.
Bulletin of Informatics and Cybernetics 53, 1-19. (doi: 10.5109/4151124 )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 )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 )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)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)松井秀俊 (2019).
関数データに基づく統計的モデリング. (English title: Statistical modeling via functional data analysis)
統計数理 67, 73-96. (PDF)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)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)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)竹村彰通，和泉志津江，斎藤邦彦，姫野哲人，松井秀俊，伊達平和 (2018).
データサイエンス教育の滋賀大学モデル．(English title: Shiga University model of data science education)
統計数理 66(1), 63-78. (PDF)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)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).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).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).松井秀俊，三角俊裕，横溝孝明，小西貞則 (2016).
非線形混合効果モデルに基づく関数データクラスタリング． (English title: Clustering for functional data via nonlinear mixed effects models)
応用統計学 45, 25-45. (doi: 10.5023/jappstat.45.25)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)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)Matsui, H. (2015b).
Selection of decision boundaries for logistic regression.
Bulletin of Informatics and Cybernetics 47, 83-95. (doi: 10.5109/1909526)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)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)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)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)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)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)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)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)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)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)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)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)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)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)Matsui, H., Araki, Y., and Konishi, S. (2008).
Multivariate regression modeling for functional data.
Journal of Data Science 6, 313-331. (PDF)
Conference Papers (Peer reviewed)
Zeng, Y., Shimizu, S., Matsui, H. and Sun, F. (2022).
Causal discovery for linear mixed data.
1st Conference on Causal Learning and Reasoning (CLeaR 2022). (OpenReview.net)
Preprints
Uchida, A., Kameoka, T., Ise, T., Matsui, H. and Uchida. Y. (2024).
Social factors of urban greening: demographics, zoning, and social capital.
SSRN Preprint 10.2139/ssrn.4728860Doi, H., Matsui, H., Nishioka, D., Ito, Y. and Saura, R. (2024).
Visualisation of running form changes measured by wearable sensors for conditioning management, an application of the Functional Data Analysis.
Research Square Preprint 10.21203/rs.3.rs-3850139/v1Wakayama, T. and Matsui, H. (2023).
Functional data regression reconciles with excess bases.
arXiv Preprint 2308.01724Matsui, H. and Yamakawa, Y. (2023).
Sparse estimation in ordinary kriging for functional data.
arXiv Preprint 2306.15537Tanaka, S. and Matsui, H. (2023).
Variable screening using factor analysis for high-dimensional data with multicollinearity.
arXiv Preprint 2306.05702Matsui, H. (2022).
Truncated estimation for varying-coefficient functional linear model.
arXiv Preprint 2203.10268.Matsui, H. (2021).
Sparse varying-coefficient functional linear model.
arXiv Preprint 2110.12599.Matsui, H. (2020).
Varying-coefficient functional additive models.
arXiv Preprint 2005.12641.Matsui, H. (2014).
Model selection criteria for nonlinear mixed effects modeling.
arXiv Preprint 1402.5724.
Book Reviews
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.
Others
松井秀俊 (2020).
滋賀大学におけるデータサイエンス教育の展開 (English title: Development of data science education in Shiga University)．
第11回横幹連合コンファレンス予稿集，A-1-1. (doi: 10.11487/oukan.2020.0_A-1-1)González-Rodríguez, G., and Matsui, H. (2019).
(Preface) Special feature: functional data analysis and its applications. (No peer-reviewed)
Behaviormetrika 46, 145-146. (doi: 10.1007/s41237-019-00081-9)
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．