Ryoya Oda's Home page

Papers

・Published and accepted Papers

1. Yokoi, M., Abekura, H., Kagawa, K., Morita, K., Nishio, F., Umehara, H., Kato, M., Oda, R., Doi, K. & Tsuga, K. (2025). Comparison of clinical outcomes of polyetheretherketone and hybrid resin crowns placed on molars for over two years. Scientific Reports, 15, 15627.
  ▪ https://www.nature.com/articles/s41598-025-95075-x
2. Itamiya, H., Oda, R., Yanagihara, H., Kasamatsu, M. & Tsuge, K. (2024). Forensic discrimination of fiber microspectrophotometry data by resampling and repeating two-sample hypothesis testing. Microchemical Journal, 206, 111440.
  ▪ https://www.sciencedirect.com/science/article/abs/pii/S0026265X24015522
3. Sadahide, A., Ohara, H., Oda, R. & Harada, Y. (2024). Ciliochoroidal detachment after intrascleral lens fixation using the Yamane technique. Cureus, 16(8), 1-7, e66562, doi:10.7759/cureus.66562.
  ▪ https://www.cureus.com/articles/274622-ciliochoroidal-detachment-after-intrascleral-lens-fixation-using-the-yamane-technique?utm_medium=email&utm_source=transaction#!/
4. Oda, R., Yanagihara, H. & Fujikoshi, Y. (2024). On model selection consistency using a kick-one-out method for selecting response variables in high-dimensional multivariate linear regression. Comm. Statist. Theory Methods., 1-15, doi: 10.1080/03610926.2024.2370914.
  ▪ https://www.tandfonline.com/doi/full/10.1080/03610926.2024.2370914
5. Sadahide, A., Itoh, H., Moritou, K., Kameyama, H., Oda, R., Tabuchi, H. & Kiuchi, Y. (2024). A clinical trial evaluating the efficacy of deep learning-based facial recognition for patient identification in diverse hospital settings. Bioengineering, 11, 384, 1-11, doi: 10.3390/bioengineering11040384.
  ▪ https://www.mdpi.com/2306-5354/11/4/384
6. Ito, Y., Fukui, K. & Oda, R. (2023). Interactions between junior high school students and young children in home economics class: an examination from students’ feelings toward young children. Journal of Home Economics of Japan, 74, 363-377, doi: 10.11428/jhej.74.363.
  - https://www.jstage.jst.go.jp/article/jhej/74/7/74_363/_article/-char/ja
7. Oda, R. (2023). Kick-one-out-based variable selection method using ridge-type Cp criterion in high-dimensional multi-response linear regression models. Proceedings of the 15th KES-IDT 2023 Conference (eds. Czarnowski, I., Howlett, R. J. & Jain, L. C.), Smart Innov. Syst. Tec., 352, 193-202, doi: 10.1007/978-981-99-2969-6_17.
  - https://link.springer.com/chapter/10.1007/978-981-99-2969-6_17
8. 尾原健太, 氏間和仁, 小田凌也, 福井敬祐 (2023). デジタル・リーディングにおけるスクロール方略の分類と判別アルゴリズムの検討. 日本教育工学会論文誌, 47, 197-205, doi: 10.15077/jjet.46065.
  - https://www.jstage.jst.go.jp/article/jjet/47/2/47_46065/_article/-char/ja/
9. Oda, R., Ohishi, M., Suzuki, Y. & Yanagihara, H. (2022). An ℓ_2,0-norm constrained matrix optimization via extended discrete first-order algorithms. Hiroshima Math. J., 53(3), 251-267, doi: 10.32917/h2021058.
  ▪ https://projecteuclid.org/journals/hiroshima-mathematical-journal/volume-53/issue-3/An-%e2%84%9320-norm-constrained-matrix-optimization-via-extended-discrete-%ef%ac%81rst/10.32917/h2021058.full
10. Oda, R. & Yanagihara, H. (2021). A consistent likelihood-based variable selection method in normal multivariate linear regression. Proceedings of the 13th KES-IDT 2021 Conference (eds. Czarnowski, I., Howlett, R. J. & Jain, L. C.), Smart Innov. Syst. Tec., 238, 391-401.
  - https://link.springer.com/chapter/10.1007/978-981-16-2765-1_33
11. Yanagihara, H. & Oda, R. (2021). Coordinate descent algorithm for normal-likelihood based group Lasso in multivariate linear regression. Proceedings of the 13th KES-IDT 2021 Conference (eds. Czarnowski, I., Howlett, R. J. & Jain, L. C.), Smart Innov. Syst. Tec., 238, 429-439.
  - https://link.springer.com/chapter/10.1007/978-981-16-2765-1_36
12. Imori, S., von Rosen, D. & Oda, R. (2021). Growth curve model with bilinear random coefficients. Sankhya A , 83 (1), 109-127.
  - https://link.springer.com/article/10.1007/s13171-020-00204-5
13. Oda, R., Mima, Y., Yanagihara, H. & Fujikoshi, Y. (2021). A high-dimensional bias-corrected AIC for selecting response variables in multivariate calibration. Comm. Statist. Theory Methods., 50, 3453-3476.
  - https://doi.org/10.1080/03610926.2019.1705978
14. Oda, R., Yanagihara, H. & Fujikoshi, Y. (2021). Strong consistency of log-likelihood-based information criterion in high-dimensional canonical correlation analysis. Sankhya A., 83, 109-127.
  - https://doi.org/10.1007/s13171-019-00174-3
15. Oda, R. (2020). Consistent variable selection criteria in multivariate linear regression even when dimension exceeds sample size. Hiroshima Math. J., 50 (3), 339-374.
  - https://projecteuclid.org/journals/hiroshima-mathematical-journal/volume-50/issue-3/Consistent-variable-selection-criteria-in-multivariate-linear-regression-even-when/10.32917/hmj/1607396493.full
16. Oda, R. & Yanagihara, H. (2020). A fast and consistent variable selection method for high-dimensional multivariate linear regression with a large number of explanatory variables. Electron. J. Statist., 14, 1386-1412.
  - https://projecteuclid.org/euclid.ejs/1585360818
17. Oda, R., Suzuki, Y., Yanagihara, H. & Fujikoshi, Y. (2020). A consistent variable selection method in high-dimensional canonical discriminant analysis. J. Multivariate Anal., 175, 104561-1-13.
  - https://www.sciencedirect.com/science/article/pii/S0047259X19300545
18. Oda, R., Yanagihara, H. & Fujikoshi, Y. (2019). Asymptotic null and non-null distributions of test statistics for redundancy in high-dimensional canonical correlation analysis. Random Matrices-Theo., 8, 1950001-1-26.
  - https://www.worldscientific.com/doi/abs/10.1142/S2010326319500011
19. Yanagihara, H., Oda, R., Hashiyama, Y. & Fujikoshi, Y. (2017). High-Dimensional asymptotic behaviors of the differences between the log-determinants of two Wishart matrices. J. Multivariate Anal., 157, 70–86.
  - https://www.sciencedirect.com/science/article/pii/S0047259X17301355

・Working Papers

1. Ohishi, M. & Oda, R. (2025). KOO Method-based Consistent Clustering for Group-wise Linear Regression with Graph Structure.
  ▪ https://arxiv.org/abs/2509.11103
2. 小田凌也, 栁原宏和. (2017). 異なったGMANOVA モデルにおける最小重み付き残差平方和の差の分布形. 京都大学数理解析研究所講究録, No. 2047: 107-123.
  - http://www.kurims.kyoto-u.ac.jp/~kyodo/kokyuroku/contents/2047.html

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