RYOYA ODA

Last Updated :2024/07/03

Affiliations, Positions
Graduate School of Advanced Science and Engineering, Assistant Professor
E-mail
ryoya-odahiroshima-u.ac.jp
Other Contact Details
1-3-1 Kagamiyama, Higashi-Hiroshima city, Hiroshima 739-8526, JAPAN, Japan
TEL : (+81)+81) 82-424 FAX : (+81)

Basic Information

Major Professional Backgrounds

  • 2022/04/01, Hiroshima University, Graduate School of Advanced Science and Engineering, Assistant Professor
  • 2019/05/01, 2022/03/31, Hiroshima University, School ofonformatics and Date Science, Assistant professor(Special Appointment)
  • 2018/04/01, 2019/04/30, Japan Society for the Promotion of Science, Special Postdoctoral Researcher(DC2)

Educational Backgrounds

  • Hiroshima University, Graduate School of Science , Japan, 2017/04, 2020/03
  • Hiroshima University, Graduate School of Science , Japan, 2015/04, 2017/03
  • Hiroshima University, School of Science, Japan, 2011/04, 2015/03
  • Hiroshima University, 2017/03/23

Academic Degrees

  • Hiroshima University
  • Hiroshima University

Educational Activity

  • [Bachelor Degree Program] School of Science : Mathematics : Mathematics
  • [Master's Program] Graduate School of Advanced Science and Engineering : Division of Advanced Science and Engineering : Mathematics Program
  • [Doctoral Program] Graduate School of Advanced Science and Engineering : Division of Advanced Science and Engineering : Mathematics Program

In Charge of Primary Major Programs

  • Mathematics

Research Fields

  • Informatics;Principles of Informatics;Statistical science

Research Keywords

  • Multivariate analysis; model selection; asymptotic theory

Affiliated Academic Societies

  • The Japan Statistical Society

Educational Activity

Course in Charge

  1. 2024, Liberal Arts Education Program1, 4Term, Fundamental Data Science
  2. 2024, Liberal Arts Education Program1, Second Semester, Foundamental Data Science
  3. 2024, Undergraduate Education, Intensive, Basic mathematics
  4. 2024, Liberal Arts Education Program1, 1Term, Introductory Seminar for First-Year Students
  5. 2024, Undergraduate Education, 2Term, Exercises in Probability and Mathematical Statistics A
  6. 2024, Undergraduate Education, First Semester, Special Study of Mathematics and Informatics for Graduation
  7. 2024, Undergraduate Education, Second Semester, Special Study of Mathematics and Informatics for Graduation
  8. 2024, Graduate Education (Master's Program) , Academic Year, Mathematical Statistics Seminar
  9. 2024, Graduate Education (Master's Program) , 1Term, Probability and Mathematical Statistics B
  10. 2024, Graduate Education (Master's Program) , Academic Year, Exercises in Mathematics
  11. 2024, Graduate Education (Master's Program) , Academic Year, Seminar in Mathematics

Research Activities

Academic Papers

  1. On model selection consistency using a kick-one-out method for selecting response variables in high-dimensional multivariate linear regression, Comm. Statist. Theory Methods., 2024
  2. A Clinical Trial Evaluating the Efficacy of Deep Learning-Based Facial Recognition for Patient Identification in Diverse Hospital Settings, BIOENGINEERING-BASEL, 11(4), 202404
  3. An ℓ_2,0-norm constrained matrix optimization via extended discrete first-order algorithms, HIROSHIMA MATHEMATICAL JOURNAL, 53(3), 251-267, 202311
  4. ★, 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, 2023
  5. 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, 2023
  6. Growth Curve Model with Bilinear Random Coefficients, SANKHYA-SERIES A-MATHEMATICAL STATISTICS AND PROBABILITY, 84(2), 477-508, 202208
  7. 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 Innovation, Systems and Technologies, 238, 429-439, 2021
  8. 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 Innovation, Systems and Technologies, 238, 391-401, 2021
  9. High-Dimensional asymptotic behaviors of differences between the log-determinants of two Wishart matrices, J. Multivariate Anal., 157, 70-86, 2017
  10. Asymptotic null and non-null distributions of test statistics for redundancy in high-dimensional canonical correlation analysis, Random Matrices-Theo., 1950001, 1-26, 2019
  11. A consistent variable selection method in high-dimensional canonical discriminant analysis, JOURNAL OF MULTIVARIATE ANALYSIS, 175, 202001
  12. ★, A fast and consistent variable selection method for high-dimensional multivariate linear regression with a large number of explanatory variables, ELECTRONIC JOURNAL OF STATISTICS, 14(1), 1386-1412, 2020
  13. Consistent variable selection criteria in multivariate linear regression even when dimension exceeds sample size, HIROSHIMA MATHEMATICAL JOURNAL, 50(3), 339-374, 202011
  14. Strong Consistency of Log-Likelihood-Based Information Criterion in High-Dimensional Canonical Correlation Analysis, SANKHYA-SERIES A-MATHEMATICAL STATISTICS AND PROBABILITY, 83(1), 109-127, 202102
  15. A high-dimensional bias-corrected AIC for selecting response variables in multivariate calibration, COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 50(14), 3453-3476, 20210718

Invited Lecture, Oral Presentation, Poster Presentation

  1. Kick-one-out-based variable selection method using ridge-type Cp criterion in high-dimensional multi-response linear regression models, Ryoya Oda, 15th International KES Conference, IDT-23, 2023, With Invitation, English
  2. A consistent variable selection method in the high-dimensional multiple responses linear regression, Ryoya Oda, The 5th Institute of Mathematical Statistics Asia Pacific Rim Meeting, 2018, Without Invitation, English
  3. Consistency of variable selection criteria in high-dimensional multiple responses linear regression, Ryoya Oda, 2020, Without Invitation, Japanese
  4. Condition of GIC to the model minimizing KL-loss function in high-dimensional multivariate linear regression, Ryoya Oda, Hirokazu Yanagihara, 5th International Conference on Econometrics and Statistics (EcoSta 2022), 2022, With Invitation, Japanese
  5. Asymptotically KL-loss efficiency of GIC in normal multivariate linear regression models under the high-dimensional asymptotic framework, Ryoya Oda, Hirokazu Yanagihara, 2021, Without Invitation, Japanese
  6. A consistent variable selection method with GIC in multivariate linear regression even when dimensions are large, Ryoya Oda, Hirokazu Yanagihara, 4th International Conference on Econometrics and Statistics (EcoSta 2021), 2021, With Invitation, English
  7. A consistent likelihood-based variable selection method in normal multivariate linear regression, Ryoya Oda, Hirokazu Yanagihara, 13th International KES Conference, IDT-21, 2021, With Invitation, English

External Funds

Acceptance Results of Competitive Funds

  1. 2022
  2. 2023
  3. KAKENHI(Grant-in-Aid for Early-Career Scientists), 2020, 2022