広島大学

Basic Information

Educational Backgrounds

• Hiroshima University, Graduate School of Science , Department of Mathematics, Japan, 2012/04, 2014/09
• Hiroshima University, Graduate School of Science , Department of Mathematics, Japan, 2010/04, 2012/03

Academic Degrees

• Master of Science, Hiroshima University
• Doctor of Science, Hiroshima University

Research Fields

• Informatics;Principles of Informatics;Statistical science

Research Keywords

• Mathematical Statistics

Educational Activity

Course in Charge

1. 2024, Liberal Arts Education Program1, 4Term, Fundamental Data Science
2. 2024, Graduate Education (Master's Program) , 1Term, Data Literacy
3. 2024, Liberal Arts Education Program1, 1Term, Introductory Seminar for First-Year Students
4. 2024, Undergraduate Education, 2Term, Probability and Mathematical Statistics A
5. 2024, Undergraduate Education, 1Term, Topics in Probability and Mathematical Statistics
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) , First Semester, Exercises in Mathematics A
12. 2024, Graduate Education (Master's Program) , Second Semester, Exercises in Mathematics B
13. 2024, Graduate Education (Master's Program) , Academic Year, Seminar in Mathematics

Research Activities

Academic Papers

1. Bias-corrected AIC for selecting variables in multinomial logistic regression models, Linear Algebra and its Applications, 436(11), 4329-4341, 20120601
2. Simple formula for calculating bias-corrected AIC in generalized linear models, Scandinavian Journal of Statistics, 41(2), 535-555, 201406
3. Lasso penalized model selection criteria for high-dimensional multivariate linear regression analysis, Journal of Multivariate Analysis, 132, 138-150, 201411
4. Covariance components selection in high-dimensional growth curve model with random coefficients, Journal of Multivariate Analysis, 136, 86-94, 201504
5. Consistent selection of working correlation structure in GEE analysis based on Stein’s loss function, Hiroshima Mathematical Journal, 45(1), 91-107, 201503
6. Model selection criterion based on the multivariate quasi-likelihood for generalized estimating equations, Scandinavian Journal of Statistics, 42(4), 1214-1224, 201512
7. A study on the bias-correction effect of the AIC for selecting variables in normal multivariate linear regression models under model misspecification, REVSTAT–Statistical Journal, 15(3), 299-332, 201707
8. Model selection criterion based on the prediction mean squared error in generalized estimating equations, Hiroshima Mathematical Journal, 48(3), 307-334, 201811
9. An information criterion for auxiliary variable selection in incomplete data analysis, Entropy, 21(3), 201903
10. Statistical inference for estimating the incidence of cancer at the prefectural level in Japan, Japanese Journal of Clinical Oncology, 49(5), 481-485, 201905
11. On the mean and dispersion of the Moore-Penrose generalized inverse of a Wishart matrix, Electronic Journal of Linear Algebra, 36, 124-133, 202003
12. Upper and Lower Bounds of the Dispersion of a Mean Estimator in the Growth Curve Model, Statistics and Applications, 18(2), 35-44, 202011
13. Self-consistent quantum tomography with regularization, Physical Review A, 103(6), 202106
14. Growth Curve Model with Bilinear Random Coefficients, Sankhya A, 84(2), 477-508, 202208
    Link to Paper Search Results by DOI
15. Asymptotic optimality of Cp-type criteria in high-dimensional multivariate linear regression models, Statistica Sinica, 2023

Invited Lecture, Oral Presentation, Poster Presentation

1. Growth curve model with bilinear random coefficient, Imori, S., von Rosen, D. & Oda, R., CMStatistics, 2018/12/15, With Invitation, English, Italy (University of Pisa, Pisa)
2. Orthogonal greedy algorithm under covariate shift, Imori, S., The International Conference on Trends and Perspectives in Linear Statistical Inference, 2018/08/21, With Invitation, English, Poland (Bedlewo Conference center, Bedlewo)
3. Convergence rate of OGA under covariate shift, Imori, S., The 5th Institute of Mathematical Statistics Asia Pacific Rim Meeting, 2018/06/28, With Invitation, English, Singapore (National University of Singapore, Singapore)
4. Asymptotic efficiency of Cp-type criterion in high-dimensional multivariate linear regression models, Imori, S., The 3rd International Conference on Econometrics and Statistics (EcoSta 2019), 2019/06/25, With Invitation, English, Taiwan (National Chung Hsing University, Taichung)
5. Convergence rate of importance weighted orthogonal greedy algorithm, Imori, S., The 11th ICSA International Conference, 2019/12/21, With Invitation, English, China (Hangzhou Dragon Hotel, Hangzhou)
6. Variable selection in high-dimensional multivariate linear regression models, Shinpei Imori, MSJ Autumn Meeting 2022, 2022/09/15, With Invitation, Japanese

External Funds

Acceptance Results of Competitive Funds

1. KAKENHI(Grant-in-Aid for Young Scientists (B)), 2017, 2020
2. KAKENHI(Grant-in-Aid for Early-Career Scientists), 2020, 2023