Journal Articles (Refereed)  

1. Solvang, H. K., Imori, S., Biuw, M., Lindstrøm, U. and Haug, T. (2024). Categorical data analysis using discretization of continuous variables to investigate associations in marine ecosystems. Environmetrics, 35(6), e2867.  [URL] 

2. Fukutani, T., Yoshioka, Y., Imori, S., Yanagihara, H., Sumi, K., Myoken, Y., Fujita, Y.  & Yanamoto, S. (2024).  Efficacy of Episil® in patients with hematologic malignancies: a comparative study. BMC Oral Health, 24, 522. [URL]

3. Imori. S. (2023). Asymptotic optimality of Cp-type criteria in high-dimensional multivariate linear regression models. Statistica Sinica, 33,  Online Special Issue "HIGH-DIMENSIONAL STATISTICS", 1233–1248. [URL]

4. Imori, S., von Rosen, D. & Oda, R. (2022). Growth Curve Model with Bilinear Random Coefficients. Sankhya A, 84, pages 477–508. [URL]

5. Sugiyama, T., Imori, S. & Tanaka, F. (2021). Self-consistent quantum tomography with regularization. Physical Review A, 103(6), 062615. [URL]

6. Imori, S. & von Rosen, D. (2020). Upper and Lower Bounds of the Dispersion of a Mean Estimator in the Growth Curve Model. Statistics and Applications, Special Issue "Challenges and Opportunities in Statistical Data Designing and Inference in the Emerging Global Scenario", 18(2), 35-44. [URL]

7. Imori, S. & von Rosen, D. (2020). On the mean and dispersion of the Moore-Penrose generalized inverse of a Wishart matrix. Electronic Journal of Linear Algebra, 36, 124-133. [URL]

8. Tanabe, R., Kamo, K., Fukui, K. & Imori, S. (2019). Statistical inference for estimating the incidence of cancer at the prefectural level in Japan. Japanese Journal of Clinical Oncology, 49(5), 481-485. [URL]

9. Imori, S. & Shimodaira, H. (2019). An information criterion for auxiliary variable selection in incomplete data analysis. Entropy, 21(3), 281. [URL]

10. Inatsu, Y. & Imori, S. (2018). Model selection criterion based on the prediction mean squared error in generalized estimating equations. Hiroshima Mathematical Journal, 48(3), 307-334. [URL]

11. Yanagihara, H., Kamo, K., Imori, S. & Yamamura, M. (2017). 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. [URL]

12. Imori, S. (2015). Model selection criterion based on the multivariate quasi‐likelihood for generalized estimating equations. Scandinavian Journal of Statistics, 42(4), 1214-1224. [URL]

13. Imori, S. & von Rosen, D. (2015). Covariance components selection in high-dimensional growth curve model with random coefficients. Journal of Multivariate Analysis, 136, 86-94. [URL]

14. Imori, S. (2015). Consistent selection of working correlation structure in GEE analysis based on Stein’s loss function. Hiroshima Mathematical Journal, 45(1), 91-107. [URL]

15. Katayama, S. & Imori, S. (2014). Lasso penalized model selection criteria for high-dimensional multivariate linear regression analysis. Journal of Multivariate Analysis, 132, 138-150. [URL]

16. Imori, S., Yanagihara, H. & Wakaki, H. (2014). Simple formula for calculating bias‐corrected AIC in generalized linear models. Scandinavian Journal of Statistics, 41(2), 535-555. [URL]

17. Yanagihara, H., Kamo, K. I., Imori, S. & Satoh, K. (2012). Bias-corrected AIC for selecting variables in multinomial logistic regression models. Linear Algebra and its Applications, 436(11), 4329-4341. [URL]