IPSJ Transactions on Bioinformatics, 14, 1--11, 2021. (doi:10.2197/ipsjtbio.14.1)
[8] Shinjo, K., Hara, K., Nagae, G., Umeda, T., Katsushima, K., Suzuki, M., Murofushi, Y., Umezu, Y., Takeuchi, I., Takahashi, S., Okuno, Y., Matsuo, K., Ito, H., Tajima, S., Aburatani, H., Yamato, K., anf Kondo, Y.
A novel sensitive detection method for DNA methylation in circulating free DNA of pancreatic cancer.
PLOS ONE, 15(6), 2020. (doi:10.1371/journal.pone.0233782)
[7] Matsui, H. and Umezu, Y.
Variable selection in multivariate linear models for functional data via sparse regularization.
Japanese Journal of Statistics and Data Science, 3, 453--467, 2020. (doi:10.1007/s42081-019-00055-x)
[6] Umezu, Y. and Takeuchi, I.
Selective inference via marginal screening for high dimensional classification.
Japanese Journal of Statistics and Data Science, 2, 559--589, 2019. (doi:10.1007/s42081-019-00058-8)
ジャーナル論文 (査読あり)
[9] Suzumura, S., Nakagawa, K., Umezu, U., Tsuda, K., and Takeuchi, I.
Selective Inference for High-order Interaction Features Selected in a Stepwise Manner.
[5] Umezu, Y., Shimizu, Y., Masuda, H., and Ninomiya, Y.
AIC for the non-concave penalized likelihood method.
Annals of the Institute of Statistical Mathematics, 71, 247--274, 2019. (doi:10.1007/s10463-018-0649-x)
[4] Sakuma, T., Nishi, K., Kishimoto, K., Nakagawa, K., Karasuyama, M., Umezu, Y., Kajioka, S., Yamazaki, S.J., Kimura, K.D., Matsumoto, S., Yoda, K., Fukutomi, M., Shidara, H., Ogawa, H., and Takeuchi, I.
Advanced Robotics, 33, 2019. (doi:10.1080/01691864.2019.1571438)
[3] Hirakawa, T., Yamashita, T., Tamaki, T., Fujiyoshi, H., Umezu, Y., Takeuchi, I., Matsumoto, S., and Yoda, K.
Ecosphere, 9, 2018. (doi:10.1002/ecs2.2447)
Selective Inference for Change Point Detection in Multi-dimensional Sequences.
arXiv preprint, arXiv:1706.00514, 2017.
[1] Umezu, Y. and Ninomiya, Y.
arXiv preprint, arXiv:1603.07843, 2016.
[2] Umezu, Y., Matsuoka, H., Ikeda, H., and Ninomiya, Y.
Ridge-type regularization method for questionnaire data analysis.
Pacific Journal of Mathematics for Industry, 8:5, 1--9, 2016. (doi:10.1186/s40736-016-0024-x)
[1] Umezu, Y., Matsuoka, H., Ikeda, H., and Ninomiya, Y.
Defect rate evaluation via simple active learning.
Pacific Journal of Mathematics for Industry, 7:8, 1--8, 2015. (doi:10.1186/s40736-015-0019-z)
国際会議論文 (査読あり)
[2] Yamada, M., Umezu, Y., Fukumizu, K., and Takeuchi, I.
Post Selection Inference with Kernels.
In 21st International Conference on Artificial Intelligence and Statistics, 2018.
[1] Suzumura, S., Nakagawa, K., Umezu, Y., Tsuda, K., and Takeuchi, I.
Selective Inference for Sparse High-Order Interaction Models.
In 34th International Conference on Machine Learning, 2017.
プレプリント
[3] Ninomiya, Y., Umezu, Y. and Takeuchi, I.
Selective Inference in Propensity Score Analysis.
arXiv preprint, arXiv:2105.00416, 2021.
[2] Umezu, Y. and Takeuchi, I.
査読なし論文
[1] 梅津佑太.
京都大学 数理研 研究集会「量子統計モデリングのための基盤構築」講究録, 2018, 2017.
書籍
[2] Bradley Efron, Trevor Hastie (著)，藤澤洋徳，井手剛 (監訳)，井尻善久，井手剛，牛久祥孝，梅津佑太，大塚琢馬，尾林慶一，川野秀一，田栗正隆，竹内孝，橋本敦史，藤澤洋徳，矢野恵佑 (訳)
共立出版, 2020. (ISBN: 978-4-320-11434-0)
[1] 梅津佑太, 西井龍映, 上田勇祐
スパース回帰分析とパターン認識 (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, 2020. (ISBN: 978-4-06-518620-6)