Uchida, A., Kameoka, T., Ise, T., Matsui, H., and Uchida, Y. (2024).
Social factors of urban greening: Demographics, zoning, and social capital.
City and Environment Interactions, in press. (doi: 10.1016/j.cacint.2024.100160)
Shimazu, Y., Yamaguchi, T., Hoshina, I., and Matsui, H. (2024).
Variable selection for additive models with missing data via multiple imputation.
Behaviormetrika, in press. (doi: 10.1007/s41237-024-00236-3)
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.
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)
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.
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)
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)
Matsui, H. (2019).
Statistical modeling via functional data analysis. (in Japanese)
Proceedings of the Institute of Statistical Mathematics 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)
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)
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).
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)
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)
Zeng, Y., Shimizu, S., Matsui, H. and Sun, F. (2022).
Causal discovery for linear mixed data.
1st Conference on Causal Learning and Reasoning. (OpenReview.net)
Doi, 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/v1
Wakayama, T. and Matsui, H. (2023).
Functional data regression reconciles with excess bases.
arXiv Preprint 2308.01724
Matsui, H. and Yamakawa, Y. (2023).
Sparse estimation in ordinary kriging for functional data.
arXiv Preprint 2306.15537
Tanaka, S. and Matsui, H. (2023).
Variable screening using factor analysis for high-dimensional data with multicollinearity.
arXiv Preprint 2306.05702
Matsui, H. (2022).
Truncated estimation for varying-coefficient functional linear model.
arXiv Preprint 2203.10268.
Watanabe, S. and Matsui, H. (2022).
Classification from Positive and Biased Negative Data with Skewed Labeled Posterior Probability.
arXiv Preprint 2203.05749.
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.
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)
Matsui, H. (2014).
Model selection criteria for nonlinear mixed effects modeling.
arXiv Preprint 1402.5724.
Regularized Functional Regression Modeling and its Applications (2009). Doctoral Thesis, Graduate School of Mathematics, Kyushu University.